From 157955cdb5afe71338ac62ecb4fd34366f63b53a Mon Sep 17 00:00:00 2001 From: Apostolos Chalkis Date: Mon, 16 Oct 2023 16:09:25 -0600 Subject: [PATCH 01/35] Fix the warning message in R Mac's cran test (#285) * copy and replace lp_rlp.h * remove re-initialization of eta * update ubuntu version from 18 to 20 in R cran tests * minor improvements (explanatory comments) --------- Co-authored-by: Apostolos Chalkis --- .github/workflows/R-CMD-check-ubuntu.yml | 4 +- R-proj/src/sample_points.cpp | 6 +- cran_gen/genCRANpkg.R | 2 + .../lpsolve_modified_header_files/lp_rlp.h | 2460 +++++++++++++++++ 4 files changed, 2466 insertions(+), 6 deletions(-) create mode 100644 external/cmake-files/lpsolve_modified_header_files/lp_rlp.h diff --git a/.github/workflows/R-CMD-check-ubuntu.yml b/.github/workflows/R-CMD-check-ubuntu.yml index 645396120..9fe81de99 100644 --- a/.github/workflows/R-CMD-check-ubuntu.yml +++ b/.github/workflows/R-CMD-check-ubuntu.yml @@ -22,8 +22,8 @@ jobs: config: - {os: ubuntu-latest, r: 'devel'} - {os: ubuntu-latest, r: 'release'} - - {os: ubuntu-18.04, r: 'devel'} - - {os: ubuntu-18.04, r: 'release'} + - {os: ubuntu-20.04, r: 'devel'} + - {os: ubuntu-20.04, r: 'release'} env: R_REMOTES_NO_ERRORS_FROM_WARNINGS: true diff --git a/R-proj/src/sample_points.cpp b/R-proj/src/sample_points.cpp index d03c9b33f..b1150877f 100644 --- a/R-proj/src/sample_points.cpp +++ b/R-proj/src/sample_points.cpp @@ -346,7 +346,7 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, random_walks walk; ode_solvers solver; // Used only for logconcave sampling - NT eta; + NT eta = 1; std::list randPoints; std::pair InnerBall; Point mode(dim); @@ -410,7 +410,7 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, Rcpp::Function negative_logprob = Rcpp::as(distribution)["negative_logprob"]; Rcpp::Function negative_logprob_gradient = Rcpp::as(distribution)["negative_logprob_gradient"]; - NT L_, m, eta; + NT L_, m; if (Rcpp::as(distribution).containsElementNamed("L_")) { L_ = Rcpp::as(Rcpp::as(distribution)["L_"]); @@ -459,8 +459,6 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, functor_defined = false; - NT eta; - if (Rcpp::as(random_walk).containsElementNamed("step_size")) { eta = NT(Rcpp::as(Rcpp::as(random_walk)["step_size"])); if (eta <= NT(0)) { diff --git a/cran_gen/genCRANpkg.R b/cran_gen/genCRANpkg.R index f12905f06..e4f6008be 100644 --- a/cran_gen/genCRANpkg.R +++ b/cran_gen/genCRANpkg.R @@ -42,6 +42,8 @@ dir_lp = paste0(path,"/lpSolveAPI/inst/include") h_files = dir(dir_lp, "*.h", ignore.case = TRUE, all.files = TRUE) lp_dist = paste0(path,"/external/LPsolve_src/include") file.copy(file.path(dir_lp, h_files), lp_dist, recursive=TRUE, overwrite=TRUE) +# replace the lpsolve header file that issues a warning in mac's cran test +file.copy(paste0(path,"/external/cmake-files/lpsolve_modified_header_files/lp_rlp.h"), lp_dist, recursive=TRUE, overwrite=TRUE) dir_lp = paste0(path,"/lpSolve/src") h_files = dir(dir_lp, "*.h", ignore.case = TRUE, all.files = TRUE) lp_dist = paste0(path,"/external/LPsolve_src/run_headers") diff --git a/external/cmake-files/lpsolve_modified_header_files/lp_rlp.h b/external/cmake-files/lpsolve_modified_header_files/lp_rlp.h new file mode 100644 index 000000000..058b45232 --- /dev/null +++ b/external/cmake-files/lpsolve_modified_header_files/lp_rlp.h @@ -0,0 +1,2460 @@ +// Copyright(c) 2016-2018 Kjell Konis . +// Version: 5.5.2.0-17 +// Description: The lpSolveAPI package provides an R interface to 'lp_solve', +// a Mixed Integer Linear Programming (MILP) solver with support for pure +// linear, (mixed) integer/binary, semi-continuous and special ordered sets +// (SOS) models. +// License: LGPL-2 +// Repository: CRAN + + +#define YY_INT_ALIGNED short int + +/* A lexical scanner generated by flex */ + +#define FLEX_SCANNER +#define YY_FLEX_MAJOR_VERSION 2 +#define YY_FLEX_MINOR_VERSION 5 +#define YY_FLEX_SUBMINOR_VERSION 35 +#if YY_FLEX_SUBMINOR_VERSION > 0 +#define FLEX_BETA +#endif + +/* First, we deal with platform-specific or compiler-specific issues. */ + +/* begin standard C headers. */ +#include +#include +#include +#include + +/* end standard C headers. */ + +/* flex integer type definitions */ + +#ifndef FLEXINT_H +#define FLEXINT_H + +/* C99 systems have . Non-C99 systems may or may not. */ + +#if defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + +/* C99 says to define __STDC_LIMIT_MACROS before including stdint.h, + * if you want the limit (max/min) macros for int types. + */ +#ifndef __STDC_LIMIT_MACROS +#define __STDC_LIMIT_MACROS 1 +#endif + +#include +typedef int8_t flex_int8_t; +typedef uint8_t flex_uint8_t; +typedef int16_t flex_int16_t; +typedef uint16_t flex_uint16_t; +typedef int32_t flex_int32_t; +typedef uint32_t flex_uint32_t; +#else +typedef signed char flex_int8_t; +typedef short int flex_int16_t; +typedef int flex_int32_t; +typedef unsigned char flex_uint8_t; +typedef unsigned short int flex_uint16_t; +typedef unsigned int flex_uint32_t; +#endif /* ! C99 */ + +/* Limits of integral types. */ +#ifndef INT8_MIN +#define INT8_MIN (-128) +#endif +#ifndef INT16_MIN +#define INT16_MIN (-32767-1) +#endif +#ifndef INT32_MIN +#define INT32_MIN (-2147483647-1) +#endif +#ifndef INT8_MAX +#define INT8_MAX (127) +#endif +#ifndef INT16_MAX +#define INT16_MAX (32767) +#endif +#ifndef INT32_MAX +#define INT32_MAX (2147483647) +#endif +#ifndef UINT8_MAX +#define UINT8_MAX (255U) +#endif +#ifndef UINT16_MAX +#define UINT16_MAX (65535U) +#endif +#ifndef UINT32_MAX +#define UINT32_MAX (4294967295U) +#endif + +#endif /* ! FLEXINT_H */ + +#ifdef __cplusplus + +/* The "const" storage-class-modifier is valid. */ +#define YY_USE_CONST + +#else /* ! __cplusplus */ + +/* C99 requires __STDC__ to be defined as 1. */ +#if defined (__STDC__) + +#define YY_USE_CONST + +#endif /* defined (__STDC__) */ +#endif /* ! __cplusplus */ + +#ifdef YY_USE_CONST +#define lp_yyconst const +#else +#define lp_yyconst +#endif + +/* Returned upon end-of-file. */ +#define YY_NULL 0 + +/* Promotes a possibly negative, possibly signed char to an unsigned + * integer for use as an array index. If the signed char is negative, + * we want to instead treat it as an 8-bit unsigned char, hence the + * double cast. + */ +#define YY_SC_TO_UI(c) ((unsigned int) (unsigned char) c) + +/* An opaque pointer. */ +#ifndef YY_TYPEDEF_YY_SCANNER_T +#define YY_TYPEDEF_YY_SCANNER_T +typedef void* lp_yyscan_t; +#endif + +/* For convenience, these vars (plus the bison vars far below) + are macros in the reentrant scanner. */ +#define lp_yyin lp_yyg->lp_yyin_r +#define lp_yyout lp_yyg->lp_yyout_r +#define lp_yyextra lp_yyg->lp_yyextra_r +#define lp_yyleng lp_yyg->lp_yyleng_r +#define lp_yytext lp_yyg->lp_yytext_r +#define lp_yylineno (YY_CURRENT_BUFFER_LVALUE->lp_yy_bs_lineno) +#define lp_yycolumn (YY_CURRENT_BUFFER_LVALUE->lp_yy_bs_column) +#define lp_yy_flex_debug lp_yyg->lp_yy_flex_debug_r + +/* Enter a start condition. This macro really ought to take a parameter, + * but we do it the disgusting crufty way forced on us by the ()-less + * definition of BEGIN. + */ +#define BEGIN lp_yyg->lp_yy_start = 1 + 2 * + +/* Translate the current start state into a value that can be later handed + * to BEGIN to return to the state. The YYSTATE alias is for lex + * compatibility. + */ +#define YY_START ((lp_yyg->lp_yy_start - 1) / 2) +#define YYSTATE YY_START + +/* Action number for EOF rule of a given start state. */ +#define YY_STATE_EOF(state) (YY_END_OF_BUFFER + state + 1) + +/* Special action meaning "start processing a new file". */ +#define YY_NEW_FILE lp_yyrestart(lp_yyin ,lp_yyscanner ) + +#define YY_END_OF_BUFFER_CHAR 0 + +/* Size of default input buffer. */ +#ifndef YY_BUF_SIZE +#define YY_BUF_SIZE 16384 +#endif + +/* The state buf must be large enough to hold one state per character in the main buffer. + */ +#define YY_STATE_BUF_SIZE ((YY_BUF_SIZE + 2) * sizeof(lp_yy_state_type)) + +#ifndef YY_TYPEDEF_YY_BUFFER_STATE +#define YY_TYPEDEF_YY_BUFFER_STATE +typedef struct lp_yy_buffer_state *YY_BUFFER_STATE; +#endif + +#define EOB_ACT_CONTINUE_SCAN 0 +#define EOB_ACT_END_OF_FILE 1 +#define EOB_ACT_LAST_MATCH 2 + + /* Note: We specifically omit the test for lp_yy_rule_can_match_eol because it requires + * access to the local variable lp_yy_act. Since lp_yyless() is a macro, it would break + * existing scanners that call lp_yyless() from OUTSIDE lp_yylex. + * One obvious solution it to make lp_yy_act a global. I tried that, and saw + * a 5% performance hit in a non-lp_yylineno scanner, because lp_yy_act is + * normally declared as a register variable-- so it is not worth it. + */ + #define YY_LESS_LINENO(n) \ + do { \ + int lp_yyl;\ + for ( lp_yyl = n; lp_yyl < lp_yyleng; ++lp_yyl )\ + if ( lp_yytext[lp_yyl] == '\n' )\ + --lp_yylineno;\ + }while(0) + +/* Return all but the first "n" matched characters back to the input stream. */ +#define lp_yyless(n) \ + do \ + { \ + /* Undo effects of setting up lp_yytext. */ \ + int lp_yyless_macro_arg = (n); \ + YY_LESS_LINENO(lp_yyless_macro_arg);\ + *lp_yy_cp = lp_yyg->lp_yy_hold_char; \ + YY_RESTORE_YY_MORE_OFFSET \ + lp_yyg->lp_yy_c_buf_p = lp_yy_cp = lp_yy_bp + lp_yyless_macro_arg - YY_MORE_ADJ; \ + YY_DO_BEFORE_ACTION; /* set up lp_yytext again */ \ + } \ + while ( 0 ) + +#define unput(c) lp_yyunput( c, lp_yyg->lp_yytext_ptr , lp_yyscanner ) + +#ifndef YY_TYPEDEF_YY_SIZE_T +#define YY_TYPEDEF_YY_SIZE_T +typedef size_t lp_yy_size_t; +#endif + +#ifndef YY_STRUCT_YY_BUFFER_STATE +#define YY_STRUCT_YY_BUFFER_STATE +struct lp_yy_buffer_state + { + FILE *lp_yy_input_file; + + char *lp_yy_ch_buf; /* input buffer */ + char *lp_yy_buf_pos; /* current position in input buffer */ + + /* Size of input buffer in bytes, not including room for EOB + * characters. + */ + lp_yy_size_t lp_yy_buf_size; + + /* Number of characters read into lp_yy_ch_buf, not including EOB + * characters. + */ + int lp_yy_n_chars; + + /* Whether we "own" the buffer - i.e., we know we created it, + * and can realloc() it to grow it, and should free() it to + * delete it. + */ + int lp_yy_is_our_buffer; + + /* Whether this is an "interactive" input source; if so, and + * if we're using stdio for input, then we want to use getc() + * instead of fread(), to make sure we stop fetching input after + * each newline. + */ + int lp_yy_is_interactive; + + /* Whether we're considered to be at the beginning of a line. + * If so, '^' rules will be active on the next match, otherwise + * not. + */ + int lp_yy_at_bol; + + int lp_yy_bs_lineno; /**< The line count. */ + int lp_yy_bs_column; /**< The column count. */ + + /* Whether to try to fill the input buffer when we reach the + * end of it. + */ + int lp_yy_fill_buffer; + + int lp_yy_buffer_status; + +#define YY_BUFFER_NEW 0 +#define YY_BUFFER_NORMAL 1 + /* When an EOF's been seen but there's still some text to process + * then we mark the buffer as YY_EOF_PENDING, to indicate that we + * shouldn't try reading from the input source any more. We might + * still have a bunch of tokens to match, though, because of + * possible backing-up. + * + * When we actually see the EOF, we change the status to "new" + * (via lp_yyrestart()), so that the user can continue scanning by + * just pointing lp_yyin at a new input file. + */ +#define YY_BUFFER_EOF_PENDING 2 + + }; +#endif /* !YY_STRUCT_YY_BUFFER_STATE */ + +/* We provide macros for accessing buffer states in case in the + * future we want to put the buffer states in a more general + * "scanner state". + * + * Returns the top of the stack, or NULL. + */ +#define YY_CURRENT_BUFFER ( lp_yyg->lp_yy_buffer_stack \ + ? lp_yyg->lp_yy_buffer_stack[lp_yyg->lp_yy_buffer_stack_top] \ + : NULL) + +/* Same as previous macro, but useful when we know that the buffer stack is not + * NULL or when we need an lvalue. For internal use only. + */ +#define YY_CURRENT_BUFFER_LVALUE lp_yyg->lp_yy_buffer_stack[lp_yyg->lp_yy_buffer_stack_top] + +void lp_yyrestart (FILE *input_file ,lp_yyscan_t lp_yyscanner ); +void lp_yy_switch_to_buffer (YY_BUFFER_STATE new_buffer ,lp_yyscan_t lp_yyscanner ); +YY_BUFFER_STATE lp_yy_create_buffer (FILE *file,int size ,lp_yyscan_t lp_yyscanner ); +void lp_yy_delete_buffer (YY_BUFFER_STATE b ,lp_yyscan_t lp_yyscanner ); +void lp_yy_flush_buffer (YY_BUFFER_STATE b ,lp_yyscan_t lp_yyscanner ); +void lp_yypush_buffer_state (YY_BUFFER_STATE new_buffer ,lp_yyscan_t lp_yyscanner ); +void lp_yypop_buffer_state (lp_yyscan_t lp_yyscanner ); + +static void lp_yyensure_buffer_stack (lp_yyscan_t lp_yyscanner ); +static void lp_yy_load_buffer_state (lp_yyscan_t lp_yyscanner ); +static void lp_yy_init_buffer (YY_BUFFER_STATE b,FILE *file ,lp_yyscan_t lp_yyscanner ); + +#define YY_FLUSH_BUFFER lp_yy_flush_buffer(YY_CURRENT_BUFFER ,lp_yyscanner) + +YY_BUFFER_STATE lp_yy_scan_buffer (char *base,lp_yy_size_t size ,lp_yyscan_t lp_yyscanner ); +YY_BUFFER_STATE lp_yy_scan_string (lp_yyconst char *lp_yy_str ,lp_yyscan_t lp_yyscanner ); +YY_BUFFER_STATE lp_yy_scan_bytes (lp_yyconst char *bytes,int len ,lp_yyscan_t lp_yyscanner ); + +void *lp_yyalloc (lp_yy_size_t ,lp_yyscan_t lp_yyscanner ); +void *lp_yyrealloc (void *,lp_yy_size_t ,lp_yyscan_t lp_yyscanner ); +void lp_yyfree (void * ,lp_yyscan_t lp_yyscanner ); + +#define lp_yy_new_buffer lp_yy_create_buffer + +#define lp_yy_set_interactive(is_interactive) \ + { \ + if ( ! YY_CURRENT_BUFFER ){ \ + lp_yyensure_buffer_stack (lp_yyscanner); \ + YY_CURRENT_BUFFER_LVALUE = \ + lp_yy_create_buffer(lp_yyin,YY_BUF_SIZE ,lp_yyscanner); \ + } \ + YY_CURRENT_BUFFER_LVALUE->lp_yy_is_interactive = is_interactive; \ + } + +#define lp_yy_set_bol(at_bol) \ + { \ + if ( ! YY_CURRENT_BUFFER ){\ + lp_yyensure_buffer_stack (lp_yyscanner); \ + YY_CURRENT_BUFFER_LVALUE = \ + lp_yy_create_buffer(lp_yyin,YY_BUF_SIZE ,lp_yyscanner); \ + } \ + YY_CURRENT_BUFFER_LVALUE->lp_yy_at_bol = at_bol; \ + } + +#define YY_AT_BOL() (YY_CURRENT_BUFFER_LVALUE->lp_yy_at_bol) + +/* Begin user sect3 */ + +#define lp_yywrap(n) 1 +#define YY_SKIP_YYWRAP + +typedef unsigned char YY_CHAR; + +typedef int lp_yy_state_type; + +#define lp_yytext_ptr lp_yytext_r + +static lp_yy_state_type lp_yy_get_previous_state (lp_yyscan_t lp_yyscanner ); +static lp_yy_state_type lp_yy_try_NUL_trans (lp_yy_state_type current_state ,lp_yyscan_t lp_yyscanner); +static int lp_yy_get_next_buffer (lp_yyscan_t lp_yyscanner ); +static void lp_yy_fatal_error (lp_yyconst char msg[] ,lp_yyscan_t lp_yyscanner ); + +/* Done after the current pattern has been matched and before the + * corresponding action - sets up lp_yytext. + */ +#define YY_DO_BEFORE_ACTION \ + lp_yyg->lp_yytext_ptr = lp_yy_bp; \ + lp_yyleng = (size_t) (lp_yy_cp - lp_yy_bp); \ + lp_yyg->lp_yy_hold_char = *lp_yy_cp; \ + *lp_yy_cp = '\0'; \ + lp_yyg->lp_yy_c_buf_p = lp_yy_cp; + +#define YY_NUM_RULES 33 +#define YY_END_OF_BUFFER 34 +/* This struct is not used in this scanner, + but its presence is necessary. */ +struct lp_yy_trans_info + { + flex_int32_t lp_yy_verify; + flex_int32_t lp_yy_nxt; + }; +static lp_yyconst flex_int16_t lp_yy_accept[144] = + { 0, + 0, 0, 0, 0, 0, 0, 34, 32, 10, 10, + 27, 17, 11, 32, 32, 14, 26, 31, 29, 28, + 30, 25, 25, 10, 25, 25, 25, 25, 3, 4, + 3, 3, 9, 7, 8, 10, 17, 17, 0, 15, + 1, 6, 15, 14, 0, 29, 30, 0, 25, 24, + 0, 25, 25, 10, 0, 0, 0, 0, 25, 25, + 25, 25, 25, 2, 0, 15, 0, 15, 22, 0, + 25, 25, 0, 0, 0, 0, 0, 19, 25, 18, + 20, 25, 25, 21, 0, 25, 0, 13, 25, 0, + 12, 25, 19, 0, 18, 20, 21, 25, 23, 25, + + 20, 21, 21, 16, 16, 0, 25, 25, 0, 23, + 0, 21, 25, 25, 0, 0, 25, 25, 0, 0, + 19, 25, 0, 0, 25, 25, 19, 0, 18, 0, + 0, 25, 25, 18, 0, 0, 0, 0, 0, 0, + 0, 0, 0 + } ; + +static lp_yyconst flex_int32_t lp_yy_ec[256] = + { 0, + 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, + 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 2, 1, 1, 5, 6, 5, 5, 5, 1, + 1, 7, 8, 9, 10, 11, 12, 13, 14, 14, + 13, 13, 13, 13, 13, 13, 13, 15, 16, 17, + 18, 19, 1, 5, 20, 21, 22, 23, 24, 25, + 26, 23, 27, 23, 23, 23, 28, 29, 30, 23, + 23, 31, 32, 33, 34, 23, 23, 35, 36, 37, + 5, 1, 5, 5, 5, 1, 20, 21, 22, 23, + + 24, 25, 26, 23, 27, 23, 23, 23, 28, 29, + 30, 23, 23, 31, 32, 33, 34, 23, 23, 35, + 36, 37, 5, 1, 5, 5, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1 + } ; + +static lp_yyconst flex_int32_t lp_yy_meta[38] = + { 0, + 1, 2, 3, 3, 4, 5, 6, 3, 6, 3, + 5, 5, 5, 5, 7, 6, 7, 6, 6, 4, + 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4 + } ; + +static lp_yyconst flex_int16_t lp_yy_base[150] = + { 0, + 0, 36, 36, 38, 43, 45, 366, 388, 48, 62, + 388, 338, 388, 40, 48, 60, 388, 388, 346, 388, + 326, 60, 65, 91, 81, 74, 85, 102, 388, 388, + 388, 330, 388, 388, 388, 125, 313, 134, 308, 96, + 388, 388, 117, 132, 139, 388, 388, 88, 146, 320, + 0, 149, 152, 0, 307, 301, 294, 83, 153, 156, + 157, 189, 160, 388, 286, 126, 65, 108, 388, 289, + 181, 185, 272, 273, 250, 249, 220, 199, 203, 208, + 192, 211, 219, 227, 243, 109, 163, 225, 202, 174, + 224, 215, 213, 207, 191, 388, 189, 227, 228, 231, + + 244, 240, 253, 276, 388, 170, 260, 262, 166, 388, + 169, 179, 263, 241, 166, 159, 270, 272, 149, 155, + 284, 288, 130, 124, 296, 303, 388, 103, 300, 96, + 45, 324, 328, 388, 82, 311, 79, 68, 54, 56, + 25, 12, 388, 345, 353, 360, 367, 372, 379 + } ; + +static lp_yyconst flex_int16_t lp_yy_def[150] = + { 0, + 143, 1, 144, 144, 145, 145, 143, 143, 143, 143, + 143, 146, 143, 143, 143, 143, 143, 143, 143, 143, + 143, 147, 147, 143, 147, 147, 147, 147, 143, 143, + 143, 143, 143, 143, 143, 143, 146, 143, 143, 143, + 143, 143, 143, 143, 143, 143, 143, 143, 147, 143, + 148, 147, 147, 24, 143, 143, 143, 143, 147, 147, + 147, 147, 147, 143, 143, 143, 143, 143, 143, 148, + 147, 147, 143, 143, 143, 143, 143, 147, 147, 147, + 147, 147, 147, 147, 149, 143, 143, 143, 62, 143, + 143, 62, 143, 143, 143, 143, 143, 62, 62, 62, + + 62, 62, 62, 143, 143, 143, 62, 62, 143, 143, + 143, 143, 62, 62, 143, 143, 62, 62, 143, 143, + 62, 62, 143, 143, 62, 62, 143, 143, 62, 143, + 143, 147, 147, 143, 143, 149, 143, 143, 143, 143, + 143, 143, 0, 143, 143, 143, 143, 143, 143 + } ; + +static lp_yyconst flex_int16_t lp_yy_nxt[426] = + { 0, + 8, 9, 10, 9, 8, 8, 11, 12, 13, 12, + 14, 15, 16, 16, 17, 18, 19, 20, 21, 22, + 22, 22, 22, 22, 22, 22, 22, 23, 22, 22, + 22, 22, 22, 22, 22, 22, 22, 24, 30, 31, + 30, 31, 32, 96, 32, 34, 35, 34, 35, 36, + 36, 36, 40, 40, 41, 37, 25, 37, 142, 42, + 26, 48, 27, 36, 36, 36, 48, 28, 136, 37, + 43, 37, 44, 44, 50, 48, 51, 68, 68, 50, + 136, 51, 48, 45, 52, 141, 48, 140, 50, 48, + 51, 53, 54, 36, 36, 50, 139, 51, 37, 50, + + 37, 51, 50, 48, 60, 138, 76, 59, 40, 40, + 48, 55, 77, 61, 137, 56, 50, 57, 51, 45, + 68, 68, 58, 50, 135, 62, 36, 36, 36, 66, + 66, 63, 37, 134, 37, 38, 38, 38, 66, 66, + 45, 38, 43, 38, 44, 44, 67, 48, 67, 45, + 48, 68, 68, 48, 48, 45, 131, 48, 48, 130, + 50, 48, 51, 50, 87, 51, 50, 50, 51, 51, + 50, 50, 51, 51, 50, 90, 51, 88, 128, 79, + 72, 78, 87, 71, 127, 124, 90, 123, 91, 80, + 48, 84, 112, 48, 120, 88, 119, 51, 116, 91, + + 48, 51, 112, 50, 48, 51, 50, 89, 51, 48, + 81, 92, 48, 50, 111, 51, 82, 50, 98, 51, + 48, 83, 50, 49, 51, 50, 99, 51, 48, 107, + 110, 100, 109, 50, 49, 51, 49, 101, 69, 69, + 103, 50, 108, 51, 104, 104, 104, 49, 49, 49, + 102, 97, 49, 115, 49, 49, 114, 113, 49, 49, + 49, 49, 49, 49, 122, 49, 103, 49, 49, 106, + 96, 49, 49, 49, 49, 81, 49, 104, 104, 104, + 49, 49, 95, 49, 49, 49, 117, 49, 118, 49, + 49, 49, 49, 49, 49, 49, 94, 49, 121, 49, + + 93, 125, 49, 126, 49, 49, 125, 86, 126, 49, + 85, 49, 104, 104, 104, 49, 49, 49, 129, 132, + 49, 49, 75, 49, 49, 87, 133, 49, 49, 90, + 49, 74, 49, 73, 69, 49, 65, 143, 88, 39, + 51, 64, 91, 47, 51, 29, 29, 29, 29, 29, + 29, 29, 29, 33, 33, 33, 33, 33, 33, 33, + 33, 38, 38, 46, 39, 143, 143, 38, 49, 143, + 49, 49, 143, 49, 49, 70, 70, 143, 143, 70, + 105, 105, 143, 105, 105, 105, 105, 7, 143, 143, + 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, + + 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, + 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, + 143, 143, 143, 143, 143 + } ; + +static lp_yyconst flex_int16_t lp_yy_chk[426] = + { 0, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, + 4, 4, 3, 142, 4, 5, 5, 6, 6, 9, + 9, 9, 14, 14, 15, 9, 2, 9, 141, 15, + 2, 22, 2, 10, 10, 10, 23, 2, 131, 10, + 16, 10, 16, 16, 22, 26, 22, 67, 67, 23, + 131, 23, 25, 16, 23, 140, 27, 139, 26, 48, + 26, 23, 24, 24, 24, 25, 138, 25, 24, 27, + + 24, 27, 48, 28, 26, 137, 58, 25, 40, 40, + 86, 24, 58, 27, 135, 24, 28, 24, 28, 40, + 68, 68, 24, 86, 130, 28, 36, 36, 36, 43, + 43, 28, 36, 128, 36, 38, 38, 38, 66, 66, + 43, 38, 44, 38, 44, 44, 45, 49, 45, 66, + 52, 45, 45, 53, 59, 44, 124, 60, 61, 123, + 49, 63, 49, 52, 87, 52, 53, 59, 53, 59, + 60, 61, 60, 61, 63, 90, 63, 87, 120, 60, + 53, 59, 71, 52, 119, 116, 72, 115, 90, 61, + 62, 63, 112, 81, 111, 71, 109, 71, 106, 72, + + 78, 72, 97, 62, 79, 62, 81, 71, 81, 80, + 62, 72, 82, 78, 95, 78, 62, 79, 78, 79, + 83, 62, 80, 89, 80, 82, 79, 82, 84, 89, + 94, 80, 93, 83, 89, 83, 92, 82, 91, 88, + 84, 84, 92, 84, 85, 85, 85, 92, 98, 99, + 83, 77, 100, 101, 98, 99, 100, 98, 100, 98, + 99, 102, 114, 100, 114, 101, 103, 102, 114, 85, + 76, 101, 102, 114, 103, 101, 101, 104, 104, 104, + 103, 107, 75, 108, 113, 103, 107, 107, 108, 108, + 113, 117, 107, 118, 108, 113, 74, 117, 113, 118, + + 73, 117, 117, 118, 118, 121, 117, 70, 118, 122, + 65, 121, 136, 136, 136, 122, 121, 125, 122, 125, + 122, 129, 57, 125, 126, 132, 126, 129, 125, 133, + 126, 56, 129, 55, 50, 126, 39, 136, 132, 37, + 132, 32, 133, 21, 133, 144, 144, 144, 144, 144, + 144, 144, 144, 145, 145, 145, 145, 145, 145, 145, + 145, 146, 146, 19, 12, 7, 0, 146, 147, 0, + 147, 147, 0, 147, 147, 148, 148, 0, 0, 148, + 149, 149, 0, 149, 149, 149, 149, 143, 143, 143, + 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, + + 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, + 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, + 143, 143, 143, 143, 143 + } ; + +/* Table of booleans, true if rule could match eol. */ +static lp_yyconst flex_int32_t lp_yy_rule_can_match_eol[34] = + { 0, +0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, }; + +/* The intent behind this definition is that it'll catch + * any uses of REJECT which flex missed. + */ +#define REJECT reject_used_but_not_detected +#define lp_yymore() lp_yymore_used_but_not_detected +#define YY_MORE_ADJ 0 +#define YY_RESTORE_YY_MORE_OFFSET +/* + made reentrant with help of + http://www.usualcoding.eu/post/2007/09/03/Building-a-reentrant-parser-in-C-with-Flex/Bison +*/ +/* + Note that a minimum version of flex is needed to be able to compile this. + Older version don't know the reentrant code. + Version 2.5.4 is not enough. Probably at least v2.5.31 is needed. Tested with v2.5.35 +*/ +/* +** We want the scanner to be reentrant, therefore generate no global variables. +** That what the 'reentrant' option is for. +** 'bison-bridge' is used to create a bison compatible scanner and share lp_yylval +*/ + +#define INITIAL 0 +#define COMMENT 1 +#define LINECOMMENT 2 + +#ifndef YY_NO_UNISTD_H +/* Special case for "unistd.h", since it is non-ANSI. We include it way + * down here because we want the user's section 1 to have been scanned first. + * The user has a chance to override it with an option. + */ +#include +#endif + +#ifndef YY_EXTRA_TYPE +#define YY_EXTRA_TYPE void * +#endif + +/* Holds the entire state of the reentrant scanner. */ +struct lp_yyguts_t + { + + /* User-defined. Not touched by flex. */ + YY_EXTRA_TYPE lp_yyextra_r; + + /* The rest are the same as the globals declared in the non-reentrant scanner. */ + FILE *lp_yyin_r, *lp_yyout_r; + size_t lp_yy_buffer_stack_top; /**< index of top of stack. */ + size_t lp_yy_buffer_stack_max; /**< capacity of stack. */ + YY_BUFFER_STATE * lp_yy_buffer_stack; /**< Stack as an array. */ + char lp_yy_hold_char; + int lp_yy_n_chars; + int lp_yyleng_r; + char *lp_yy_c_buf_p; + int lp_yy_init; + int lp_yy_start; + int lp_yy_did_buffer_switch_on_eof; + int lp_yy_start_stack_ptr; + int lp_yy_start_stack_depth; + int *lp_yy_start_stack; + lp_yy_state_type lp_yy_last_accepting_state; + char* lp_yy_last_accepting_cpos; + + int lp_yylineno_r; + int lp_yy_flex_debug_r; + + char *lp_yytext_r; + int lp_yy_more_flag; + int lp_yy_more_len; + + YYSTYPE * lp_yylval_r; + + }; /* end struct lp_yyguts_t */ + +static int lp_yy_init_globals (lp_yyscan_t lp_yyscanner ); + + /* This must go here because YYSTYPE and YYLTYPE are included + * from bison output in section 1.*/ + # define lp_yylval lp_yyg->lp_yylval_r + +int lp_yylex_init (lp_yyscan_t* scanner); + +int lp_yylex_init_extra (YY_EXTRA_TYPE user_defined,lp_yyscan_t* scanner); + +/* Accessor methods to globals. + These are made visible to non-reentrant scanners for convenience. */ + +int lp_yylex_destroy (lp_yyscan_t lp_yyscanner ); + +int lp_yyget_debug (lp_yyscan_t lp_yyscanner ); + +void lp_yyset_debug (int debug_flag ,lp_yyscan_t lp_yyscanner ); + +YY_EXTRA_TYPE lp_yyget_extra (lp_yyscan_t lp_yyscanner ); + +void lp_yyset_extra (YY_EXTRA_TYPE user_defined ,lp_yyscan_t lp_yyscanner ); + +FILE *lp_yyget_in (lp_yyscan_t lp_yyscanner ); + +void lp_yyset_in (FILE * in_str ,lp_yyscan_t lp_yyscanner ); + +FILE *lp_yyget_out (lp_yyscan_t lp_yyscanner ); + +void lp_yyset_out (FILE * out_str ,lp_yyscan_t lp_yyscanner ); + +int lp_yyget_leng (lp_yyscan_t lp_yyscanner ); + +char *lp_yyget_text (lp_yyscan_t lp_yyscanner ); + +int lp_yyget_lineno (lp_yyscan_t lp_yyscanner ); + +void lp_yyset_lineno (int line_number ,lp_yyscan_t lp_yyscanner ); + +YYSTYPE * lp_yyget_lval (lp_yyscan_t lp_yyscanner ); + +void lp_yyset_lval (YYSTYPE * lp_yylval_param ,lp_yyscan_t lp_yyscanner ); + +/* Macros after this point can all be overridden by user definitions in + * section 1. + */ + +#ifndef YY_SKIP_YYWRAP +#ifdef __cplusplus +extern "C" int lp_yywrap (lp_yyscan_t lp_yyscanner ); +#else +extern int lp_yywrap (lp_yyscan_t lp_yyscanner ); +#endif +#endif + + static void lp_yyunput (int c,char *buf_ptr ,lp_yyscan_t lp_yyscanner); + +#ifndef lp_yytext_ptr +static void lp_yy_flex_strncpy (char *,lp_yyconst char *,int ,lp_yyscan_t lp_yyscanner); +#endif + +#ifdef YY_NEED_STRLEN +static int lp_yy_flex_strlen (lp_yyconst char * ,lp_yyscan_t lp_yyscanner); +#endif + +#ifndef YY_NO_INPUT + +#ifdef __cplusplus +static int lp_yyinput (lp_yyscan_t lp_yyscanner ); +#else +static int input (lp_yyscan_t lp_yyscanner ); +#endif + +#endif + +/* Amount of stuff to slurp up with each read. */ +#ifndef YY_READ_BUF_SIZE +#define YY_READ_BUF_SIZE 8192 +#endif + +/* Copy whatever the last rule matched to the standard output. */ +#ifndef ECHO +/* This used to be an fputs(), but since the string might contain NUL's, + * we now use fwrite(). + */ +#define ECHO fwrite( lp_yytext, lp_yyleng, 1, lp_yyout ) +#endif + +/* Gets input and stuffs it into "buf". number of characters read, or YY_NULL, + * is returned in "result". + */ +#ifndef YY_INPUT +#define YY_INPUT(buf,result,max_size) \ + if ( YY_CURRENT_BUFFER_LVALUE->lp_yy_is_interactive ) \ + { \ + int c = '*'; \ + int n; \ + for ( n = 0; n < max_size && \ + (c = getc( lp_yyin )) != EOF && c != '\n'; ++n ) \ + buf[n] = (char) c; \ + if ( c == '\n' ) \ + buf[n++] = (char) c; \ + if ( c == EOF && ferror( lp_yyin ) ) \ + YY_FATAL_ERROR( "input in flex scanner failed" ); \ + result = n; \ + } \ + else \ + { \ + errno=0; \ + while ( (result = fread(buf, 1, max_size, lp_yyin))==0 && ferror(lp_yyin)) \ + { \ + if( errno != EINTR) \ + { \ + YY_FATAL_ERROR( "input in flex scanner failed" ); \ + break; \ + } \ + errno=0; \ + clearerr(lp_yyin); \ + } \ + }\ +\ + +#endif + +/* No semi-colon after return; correct usage is to write "lp_yyterminate();" - + * we don't want an extra ';' after the "return" because that will cause + * some compilers to complain about unreachable statements. + */ +#ifndef lp_yyterminate +#define lp_yyterminate() return YY_NULL +#endif + +/* Number of entries by which start-condition stack grows. */ +#ifndef YY_START_STACK_INCR +#define YY_START_STACK_INCR 25 +#endif + +/* Report a fatal error. */ +#ifndef YY_FATAL_ERROR +#define YY_FATAL_ERROR(msg) lp_yy_fatal_error( msg , lp_yyscanner) +#endif + +/* end tables serialization structures and prototypes */ + +/* Default declaration of generated scanner - a define so the user can + * easily add parameters. + */ +#ifndef YY_DECL +#define YY_DECL_IS_OURS 1 + +extern int lp_yylex \ + (YYSTYPE * lp_yylval_param ,lp_yyscan_t lp_yyscanner); + +#define YY_DECL int lp_yylex \ + (YYSTYPE * lp_yylval_param , lp_yyscan_t lp_yyscanner) +#endif /* !YY_DECL */ + +/* Code executed at the beginning of each rule, after lp_yytext and lp_yyleng + * have been set up. + */ +#ifndef YY_USER_ACTION +#define YY_USER_ACTION +#endif + +/* Code executed at the end of each rule. */ +#ifndef YY_BREAK +#define YY_BREAK break; +#endif + +#define YY_RULE_SETUP \ + if ( lp_yyleng > 0 ) \ + YY_CURRENT_BUFFER_LVALUE->lp_yy_at_bol = \ + (lp_yytext[lp_yyleng - 1] == '\n'); \ + YY_USER_ACTION + +/** The main scanner function which does all the work. + */ +YY_DECL +{ + register lp_yy_state_type lp_yy_current_state; + register char *lp_yy_cp, *lp_yy_bp; + register int lp_yy_act; + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + lp_yylval = lp_yylval_param; + + if ( !lp_yyg->lp_yy_init ) + { + lp_yyg->lp_yy_init = 1; + +#ifdef YY_USER_INIT + YY_USER_INIT; +#endif + + if ( ! lp_yyg->lp_yy_start ) + lp_yyg->lp_yy_start = 1; /* first start state */ + + if ( ! lp_yyin ) + lp_yyin = stdin; + + if ( ! lp_yyout ) + lp_yyout = stdout; + + if ( ! YY_CURRENT_BUFFER ) { + lp_yyensure_buffer_stack (lp_yyscanner); + YY_CURRENT_BUFFER_LVALUE = + lp_yy_create_buffer(lp_yyin,YY_BUF_SIZE ,lp_yyscanner); + } + + lp_yy_load_buffer_state(lp_yyscanner ); + } + + while ( 1 ) /* loops until end-of-file is reached */ + { + lp_yy_cp = lp_yyg->lp_yy_c_buf_p; + + /* Support of lp_yytext. */ + *lp_yy_cp = lp_yyg->lp_yy_hold_char; + + /* lp_yy_bp points to the position in lp_yy_ch_buf of the start of + * the current run. + */ + lp_yy_bp = lp_yy_cp; + + lp_yy_current_state = lp_yyg->lp_yy_start; + lp_yy_current_state += YY_AT_BOL(); +lp_yy_match: + do + { + register YY_CHAR lp_yy_c = lp_yy_ec[YY_SC_TO_UI(*lp_yy_cp)]; + if ( lp_yy_accept[lp_yy_current_state] ) + { + lp_yyg->lp_yy_last_accepting_state = lp_yy_current_state; + lp_yyg->lp_yy_last_accepting_cpos = lp_yy_cp; + } + while ( lp_yy_chk[lp_yy_base[lp_yy_current_state] + lp_yy_c] != lp_yy_current_state ) + { + lp_yy_current_state = (int) lp_yy_def[lp_yy_current_state]; + if ( lp_yy_current_state >= 144 ) + lp_yy_c = lp_yy_meta[(unsigned int) lp_yy_c]; + } + lp_yy_current_state = lp_yy_nxt[lp_yy_base[lp_yy_current_state] + (unsigned int) lp_yy_c]; + ++lp_yy_cp; + } + while ( lp_yy_base[lp_yy_current_state] != 388 ); + +lp_yy_find_action: + lp_yy_act = lp_yy_accept[lp_yy_current_state]; + if ( lp_yy_act == 0 ) + { /* have to back up */ + lp_yy_cp = lp_yyg->lp_yy_last_accepting_cpos; + lp_yy_current_state = lp_yyg->lp_yy_last_accepting_state; + lp_yy_act = lp_yy_accept[lp_yy_current_state]; + } + + YY_DO_BEFORE_ACTION; + + if ( lp_yy_act != YY_END_OF_BUFFER && lp_yy_rule_can_match_eol[lp_yy_act] ) + { + int lp_yyl; + for ( lp_yyl = 0; lp_yyl < lp_yyleng; ++lp_yyl ) + if ( lp_yytext[lp_yyl] == '\n' ) + + do{ lp_yylineno++; + lp_yycolumn=0; + }while(0) +; + } + +do_action: /* This label is used only to access EOF actions. */ + + switch ( lp_yy_act ) + { /* beginning of action switch */ + case 0: /* must back up */ + /* undo the effects of YY_DO_BEFORE_ACTION */ + *lp_yy_cp = lp_yyg->lp_yy_hold_char; + lp_yy_cp = lp_yyg->lp_yy_last_accepting_cpos; + lp_yy_current_state = lp_yyg->lp_yy_last_accepting_state; + goto lp_yy_find_action; + +case 1: +YY_RULE_SETUP +{ + BEGIN COMMENT; +} /* begin skip comment */ + YY_BREAK +case 2: +YY_RULE_SETUP +{ + BEGIN INITIAL; +} /* end skip comment */ + YY_BREAK +case 3: +YY_RULE_SETUP +{ +} + YY_BREAK +case 4: +/* rule 4 can match eol */ +YY_RULE_SETUP +{ +} + YY_BREAK +case 5: +YY_RULE_SETUP +{ +} + YY_BREAK +case 6: +YY_RULE_SETUP +{ + BEGIN LINECOMMENT; +} /* begin skip LINECOMMENT */ + YY_BREAK +case 7: +/* rule 7 can match eol */ +YY_RULE_SETUP +{ + BEGIN INITIAL; +} /* end skip LINECOMMENT */ + YY_BREAK +case 8: +YY_RULE_SETUP +{ + BEGIN INITIAL; +} /* end skip LINECOMMENT */ + YY_BREAK +case 9: +YY_RULE_SETUP +{ +} + YY_BREAK +case 10: +/* rule 10 can match eol */ +YY_RULE_SETUP +{ +} + YY_BREAK +case 11: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + + pp->lineno = lp_yylineno; + return(COMMA); +} + YY_BREAK +case 12: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + + pp->lineno = lp_yylineno; + return(MINIMISE); +} + YY_BREAK +case 13: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + + pp->lineno = lp_yylineno; + return(MAXIMISE); +} + YY_BREAK +case 14: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + + pp->lineno = lp_yylineno; + pv->f = atof((char *)lp_yytext); + return(INTCONS); +} /* f contains the last float */ + YY_BREAK +case 15: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + + pp->lineno = lp_yylineno; + pv->f = atof((char *)lp_yytext); + return(CONS); +} /* f contains the last float */ + YY_BREAK +case 16: +/* rule 16 can match eol */ +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + char *ptr, c; + + pp->lineno = lp_yylineno; + pv->f = DEF_INFINITE; + pv->Sign = 0; + ptr = (char *)lp_yytext; + while (isspace(*ptr)) ptr++; + if(*ptr == '-') + pv->Sign = 1; + if(lp_yyleng > 0) { + c = lp_yytext[lp_yyleng - 1]; + if(!isalnum(c)) + unput(c); + } + return(INF); +} /* f contains the last float */ + YY_BREAK +case 17: +/* rule 17 can match eol */ +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + int x; + + pp->lineno = lp_yylineno; + pv->Sign = 0; + for(x = 0; x < lp_yyleng; x++) + if(lp_yytext[x] == '-' || lp_yytext[x] == '+') + pv->Sign = (pv->Sign == (lp_yytext[x] == '+')); + return (TOK_SIGN); + /* Sign is TRUE if the sign-string + represents a '-'. Otherwise Sign + is FALSE */ +} + YY_BREAK +case 18: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + + pp->lineno = lp_yylineno; + if((!pv->Within_int_decl) && (!pv->Within_sec_decl) && (!pv->Within_sos_decl) && (!pv->Within_free_decl)) { + pv->Within_int_decl = 1; + pv->Within_sos_decl1 = FALSE; + } + return(SEC_INT); +} + YY_BREAK +case 19: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + + pp->lineno = lp_yylineno; + if((!pv->Within_int_decl) && (!pv->Within_sec_decl) && (!pv->Within_sos_decl) && (!pv->Within_free_decl)) { + pv->Within_int_decl = 2; + pv->Within_sos_decl1 = FALSE; + } + return(SEC_BIN); +} + YY_BREAK +case 20: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + + pp->lineno = lp_yylineno; + if((!pv->Within_int_decl) && (!pv->Within_sec_decl) && (!pv->Within_sos_decl) && (!pv->Within_free_decl)) { + pv->Within_sec_decl = TRUE; + pv->Within_sos_decl1 = FALSE; + } + return(SEC_SEC); +} + YY_BREAK +case 21: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + + pp->lineno = lp_yylineno; + if(!pv->Within_sos_decl) + pv->SOStype0 = (short)atoi(((char *)lp_yytext) + 3); + if((!pv->Within_int_decl) && (!pv->Within_sec_decl) && (!pv->Within_sos_decl) && (!pv->Within_free_decl)) + pv->Within_sos_decl = TRUE; + return(SEC_SOS); +} + YY_BREAK +case 22: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + + pp->lineno = lp_yylineno; + FREE(pv->Last_var); + pv->Last_var = strdup((char *)lp_yytext); + pv->Last_var[strlen(pv->Last_var) - 2] = 0; + return(SOSDESCR); +} + YY_BREAK +case 23: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + + pp->lineno = lp_yylineno; + if((!pv->Within_int_decl) && (!pv->Within_sec_decl) && (!pv->Within_sos_decl) && (!pv->Within_free_decl)) { + pv->Within_free_decl = TRUE; + pv->Within_sos_decl1 = FALSE; + } + return(SEC_FREE); +} + YY_BREAK +case 24: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + char *ptr; + + pp->lineno = lp_yylineno; + FREE(pv->Last_var); + pv->Last_var = strdup((char *)lp_yytext); + ptr = pv->Last_var + strlen(pv->Last_var); + ptr[-1] = ' '; + while ((--ptr >= pv->Last_var) && (isspace(*ptr))) + *ptr = 0; + return(VARIABLECOLON); +} + YY_BREAK +case 25: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + + pp->lineno = lp_yylineno; + FREE(pv->Last_var); + pv->Last_var = strdup((char *)lp_yytext); + return(VAR); +} + YY_BREAK +case 26: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + + pp->lineno = lp_yylineno; + return (COLON); +} + YY_BREAK +case 27: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + + pp->lineno = lp_yylineno; + return(AR_M_OP); +} + YY_BREAK +case 28: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + + pp->lineno = lp_yylineno; + pv->OP = *lp_yytext; + return(RE_OPEQ); +} + YY_BREAK +case 29: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + + pp->lineno = lp_yylineno; + pv->OP = *lp_yytext; + return(RE_OPLE); +} + YY_BREAK +case 30: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + + pp->lineno = lp_yylineno; + pv->OP = *lp_yytext; + return(RE_OPGE); +} + YY_BREAK +case 31: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + parse_vars *pv = (parse_vars *) pp->parse_vars; + + pp->lineno = lp_yylineno; + pv->Within_int_decl = pv->Within_sec_decl = pv->Within_sos_decl = pv->Within_free_decl = FALSE; + check_int_sec_sos_free_decl(pp, (int) pv->Within_int_decl, (int) pv->Within_sec_decl, (int) pv->Within_sos_decl, (int) pv->Within_free_decl); + return(END_C); +} + YY_BREAK +case 32: +YY_RULE_SETUP +{ + parse_parm *pp = PARM; + + pp->lineno = lp_yylineno; + report(NULL, CRITICAL, "LEX ERROR : %s lineno %d\n", lp_yytext, lp_yylineno); + return(UNDEFINED); +} + YY_BREAK +case 33: +YY_RULE_SETUP +ECHO; + YY_BREAK +case YY_STATE_EOF(INITIAL): +case YY_STATE_EOF(COMMENT): +case YY_STATE_EOF(LINECOMMENT): + lp_yyterminate(); + + case YY_END_OF_BUFFER: + { + /* Amount of text matched not including the EOB char. */ + int lp_yy_amount_of_matched_text = (int) (lp_yy_cp - lp_yyg->lp_yytext_ptr) - 1; + + /* Undo the effects of YY_DO_BEFORE_ACTION. */ + *lp_yy_cp = lp_yyg->lp_yy_hold_char; + YY_RESTORE_YY_MORE_OFFSET + + if ( YY_CURRENT_BUFFER_LVALUE->lp_yy_buffer_status == YY_BUFFER_NEW ) + { + /* We're scanning a new file or input source. It's + * possible that this happened because the user + * just pointed lp_yyin at a new source and called + * lp_yylex(). If so, then we have to assure + * consistency between YY_CURRENT_BUFFER and our + * globals. Here is the right place to do so, because + * this is the first action (other than possibly a + * back-up) that will match for the new input source. + */ + lp_yyg->lp_yy_n_chars = YY_CURRENT_BUFFER_LVALUE->lp_yy_n_chars; + YY_CURRENT_BUFFER_LVALUE->lp_yy_input_file = lp_yyin; + YY_CURRENT_BUFFER_LVALUE->lp_yy_buffer_status = YY_BUFFER_NORMAL; + } + + /* Note that here we test for lp_yy_c_buf_p "<=" to the position + * of the first EOB in the buffer, since lp_yy_c_buf_p will + * already have been incremented past the NUL character + * (since all states make transitions on EOB to the + * end-of-buffer state). Contrast this with the test + * in input(). + */ + if ( lp_yyg->lp_yy_c_buf_p <= &YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf[lp_yyg->lp_yy_n_chars] ) + { /* This was really a NUL. */ + lp_yy_state_type lp_yy_next_state; + + lp_yyg->lp_yy_c_buf_p = lp_yyg->lp_yytext_ptr + lp_yy_amount_of_matched_text; + + lp_yy_current_state = lp_yy_get_previous_state( lp_yyscanner ); + + /* Okay, we're now positioned to make the NUL + * transition. We couldn't have + * lp_yy_get_previous_state() go ahead and do it + * for us because it doesn't know how to deal + * with the possibility of jamming (and we don't + * want to build jamming into it because then it + * will run more slowly). + */ + + lp_yy_next_state = lp_yy_try_NUL_trans( lp_yy_current_state , lp_yyscanner); + + lp_yy_bp = lp_yyg->lp_yytext_ptr + YY_MORE_ADJ; + + if ( lp_yy_next_state ) + { + /* Consume the NUL. */ + lp_yy_cp = ++lp_yyg->lp_yy_c_buf_p; + lp_yy_current_state = lp_yy_next_state; + goto lp_yy_match; + } + + else + { + lp_yy_cp = lp_yyg->lp_yy_c_buf_p; + goto lp_yy_find_action; + } + } + + else switch ( lp_yy_get_next_buffer( lp_yyscanner ) ) + { + case EOB_ACT_END_OF_FILE: + { + lp_yyg->lp_yy_did_buffer_switch_on_eof = 0; + + if ( lp_yywrap(lp_yyscanner ) ) + { + /* Note: because we've taken care in + * lp_yy_get_next_buffer() to have set up + * lp_yytext, we can now set up + * lp_yy_c_buf_p so that if some total + * hoser (like flex itself) wants to + * call the scanner after we return the + * YY_NULL, it'll still work - another + * YY_NULL will get returned. + */ + lp_yyg->lp_yy_c_buf_p = lp_yyg->lp_yytext_ptr + YY_MORE_ADJ; + + lp_yy_act = YY_STATE_EOF(YY_START); + goto do_action; + } + + else + { + if ( ! lp_yyg->lp_yy_did_buffer_switch_on_eof ) + YY_NEW_FILE; + } + break; + } + + case EOB_ACT_CONTINUE_SCAN: + lp_yyg->lp_yy_c_buf_p = + lp_yyg->lp_yytext_ptr + lp_yy_amount_of_matched_text; + + lp_yy_current_state = lp_yy_get_previous_state( lp_yyscanner ); + + lp_yy_cp = lp_yyg->lp_yy_c_buf_p; + lp_yy_bp = lp_yyg->lp_yytext_ptr + YY_MORE_ADJ; + goto lp_yy_match; + + case EOB_ACT_LAST_MATCH: + lp_yyg->lp_yy_c_buf_p = + &YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf[lp_yyg->lp_yy_n_chars]; + + lp_yy_current_state = lp_yy_get_previous_state( lp_yyscanner ); + + lp_yy_cp = lp_yyg->lp_yy_c_buf_p; + lp_yy_bp = lp_yyg->lp_yytext_ptr + YY_MORE_ADJ; + goto lp_yy_find_action; + } + break; + } + + default: + YY_FATAL_ERROR( + "fatal flex scanner internal error--no action found" ); + } /* end of action switch */ + } /* end of scanning one token */ +} /* end of lp_yylex */ + +/* lp_yy_get_next_buffer - try to read in a new buffer + * + * Returns a code representing an action: + * EOB_ACT_LAST_MATCH - + * EOB_ACT_CONTINUE_SCAN - continue scanning from current position + * EOB_ACT_END_OF_FILE - end of file + */ +static int lp_yy_get_next_buffer (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + register char *dest = YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf; + register char *source = lp_yyg->lp_yytext_ptr; + register int number_to_move, i; + int ret_val; + + if ( lp_yyg->lp_yy_c_buf_p > &YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf[lp_yyg->lp_yy_n_chars + 1] ) + YY_FATAL_ERROR( + "fatal flex scanner internal error--end of buffer missed" ); + + if ( YY_CURRENT_BUFFER_LVALUE->lp_yy_fill_buffer == 0 ) + { /* Don't try to fill the buffer, so this is an EOF. */ + if ( lp_yyg->lp_yy_c_buf_p - lp_yyg->lp_yytext_ptr - YY_MORE_ADJ == 1 ) + { + /* We matched a single character, the EOB, so + * treat this as a final EOF. + */ + return EOB_ACT_END_OF_FILE; + } + + else + { + /* We matched some text prior to the EOB, first + * process it. + */ + return EOB_ACT_LAST_MATCH; + } + } + + /* Try to read more data. */ + + /* First move last chars to start of buffer. */ + number_to_move = (int) (lp_yyg->lp_yy_c_buf_p - lp_yyg->lp_yytext_ptr) - 1; + + for ( i = 0; i < number_to_move; ++i ) + *(dest++) = *(source++); + + if ( YY_CURRENT_BUFFER_LVALUE->lp_yy_buffer_status == YY_BUFFER_EOF_PENDING ) + /* don't do the read, it's not guaranteed to return an EOF, + * just force an EOF + */ + YY_CURRENT_BUFFER_LVALUE->lp_yy_n_chars = lp_yyg->lp_yy_n_chars = 0; + + else + { + int num_to_read = + YY_CURRENT_BUFFER_LVALUE->lp_yy_buf_size - number_to_move - 1; + + while ( num_to_read <= 0 ) + { /* Not enough room in the buffer - grow it. */ + + /* just a shorter name for the current buffer */ + YY_BUFFER_STATE b = YY_CURRENT_BUFFER; + + int lp_yy_c_buf_p_offset = + (int) (lp_yyg->lp_yy_c_buf_p - b->lp_yy_ch_buf); + + if ( b->lp_yy_is_our_buffer ) + { + int new_size = b->lp_yy_buf_size * 2; + + if ( new_size <= 0 ) + b->lp_yy_buf_size += b->lp_yy_buf_size / 8; + else + b->lp_yy_buf_size *= 2; + + b->lp_yy_ch_buf = (char *) + /* Include room in for 2 EOB chars. */ + lp_yyrealloc((void *) b->lp_yy_ch_buf,b->lp_yy_buf_size + 2 ,lp_yyscanner ); + } + else + /* Can't grow it, we don't own it. */ + b->lp_yy_ch_buf = 0; + + if ( ! b->lp_yy_ch_buf ) + YY_FATAL_ERROR( + "fatal error - scanner input buffer overflow" ); + + lp_yyg->lp_yy_c_buf_p = &b->lp_yy_ch_buf[lp_yy_c_buf_p_offset]; + + num_to_read = YY_CURRENT_BUFFER_LVALUE->lp_yy_buf_size - + number_to_move - 1; + + } + + if ( num_to_read > YY_READ_BUF_SIZE ) + num_to_read = YY_READ_BUF_SIZE; + + /* Read in more data. */ + YY_INPUT( (&YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf[number_to_move]), + lp_yyg->lp_yy_n_chars, (size_t) num_to_read ); + + YY_CURRENT_BUFFER_LVALUE->lp_yy_n_chars = lp_yyg->lp_yy_n_chars; + } + + if ( lp_yyg->lp_yy_n_chars == 0 ) + { + if ( number_to_move == YY_MORE_ADJ ) + { + ret_val = EOB_ACT_END_OF_FILE; + lp_yyrestart(lp_yyin ,lp_yyscanner); + } + + else + { + ret_val = EOB_ACT_LAST_MATCH; + YY_CURRENT_BUFFER_LVALUE->lp_yy_buffer_status = + YY_BUFFER_EOF_PENDING; + } + } + + else + ret_val = EOB_ACT_CONTINUE_SCAN; + + if ((lp_yy_size_t) (lp_yyg->lp_yy_n_chars + number_to_move) > YY_CURRENT_BUFFER_LVALUE->lp_yy_buf_size) { + /* Extend the array by 50%, plus the number we really need. */ + lp_yy_size_t new_size = lp_yyg->lp_yy_n_chars + number_to_move + (lp_yyg->lp_yy_n_chars >> 1); + YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf = (char *) lp_yyrealloc((void *) YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf,new_size ,lp_yyscanner ); + if ( ! YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf ) + YY_FATAL_ERROR( "out of dynamic memory in lp_yy_get_next_buffer()" ); + } + + lp_yyg->lp_yy_n_chars += number_to_move; + YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf[lp_yyg->lp_yy_n_chars] = YY_END_OF_BUFFER_CHAR; + YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf[lp_yyg->lp_yy_n_chars + 1] = YY_END_OF_BUFFER_CHAR; + + lp_yyg->lp_yytext_ptr = &YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf[0]; + + return ret_val; +} + +/* lp_yy_get_previous_state - get the state just before the EOB char was reached */ + + static lp_yy_state_type lp_yy_get_previous_state (lp_yyscan_t lp_yyscanner) +{ + register lp_yy_state_type lp_yy_current_state; + register char *lp_yy_cp; + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + lp_yy_current_state = lp_yyg->lp_yy_start; + lp_yy_current_state += YY_AT_BOL(); + + for ( lp_yy_cp = lp_yyg->lp_yytext_ptr + YY_MORE_ADJ; lp_yy_cp < lp_yyg->lp_yy_c_buf_p; ++lp_yy_cp ) + { + register YY_CHAR lp_yy_c = (*lp_yy_cp ? lp_yy_ec[YY_SC_TO_UI(*lp_yy_cp)] : 1); + if ( lp_yy_accept[lp_yy_current_state] ) + { + lp_yyg->lp_yy_last_accepting_state = lp_yy_current_state; + lp_yyg->lp_yy_last_accepting_cpos = lp_yy_cp; + } + while ( lp_yy_chk[lp_yy_base[lp_yy_current_state] + lp_yy_c] != lp_yy_current_state ) + { + lp_yy_current_state = (int) lp_yy_def[lp_yy_current_state]; + if ( lp_yy_current_state >= 144 ) + lp_yy_c = lp_yy_meta[(unsigned int) lp_yy_c]; + } + lp_yy_current_state = lp_yy_nxt[lp_yy_base[lp_yy_current_state] + (unsigned int) lp_yy_c]; + } + + return lp_yy_current_state; +} + +/* lp_yy_try_NUL_trans - try to make a transition on the NUL character + * + * synopsis + * next_state = lp_yy_try_NUL_trans( current_state ); + */ + static lp_yy_state_type lp_yy_try_NUL_trans (lp_yy_state_type lp_yy_current_state , lp_yyscan_t lp_yyscanner) +{ + register int lp_yy_is_jam; + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; /* This var may be unused depending upon options. */ + register char *lp_yy_cp = lp_yyg->lp_yy_c_buf_p; + + register YY_CHAR lp_yy_c = 1; + if ( lp_yy_accept[lp_yy_current_state] ) + { + lp_yyg->lp_yy_last_accepting_state = lp_yy_current_state; + lp_yyg->lp_yy_last_accepting_cpos = lp_yy_cp; + } + while ( lp_yy_chk[lp_yy_base[lp_yy_current_state] + lp_yy_c] != lp_yy_current_state ) + { + lp_yy_current_state = (int) lp_yy_def[lp_yy_current_state]; + if ( lp_yy_current_state >= 144 ) + lp_yy_c = lp_yy_meta[(unsigned int) lp_yy_c]; + } + lp_yy_current_state = lp_yy_nxt[lp_yy_base[lp_yy_current_state] + (unsigned int) lp_yy_c]; + lp_yy_is_jam = (lp_yy_current_state == 143); + + return lp_yy_is_jam ? 0 : lp_yy_current_state; +} + + static void lp_yyunput (int c, register char * lp_yy_bp , lp_yyscan_t lp_yyscanner) +{ + register char *lp_yy_cp; + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + lp_yy_cp = lp_yyg->lp_yy_c_buf_p; + + /* undo effects of setting up lp_yytext */ + *lp_yy_cp = lp_yyg->lp_yy_hold_char; + + if ( lp_yy_cp < YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf + 2 ) + { /* need to shift things up to make room */ + /* +2 for EOB chars. */ + register int number_to_move = lp_yyg->lp_yy_n_chars + 2; + register char *dest = &YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf[ + YY_CURRENT_BUFFER_LVALUE->lp_yy_buf_size + 2]; + register char *source = + &YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf[number_to_move]; + + while ( source > YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf ) + *--dest = *--source; + + lp_yy_cp += (int) (dest - source); + lp_yy_bp += (int) (dest - source); + YY_CURRENT_BUFFER_LVALUE->lp_yy_n_chars = + lp_yyg->lp_yy_n_chars = YY_CURRENT_BUFFER_LVALUE->lp_yy_buf_size; + + if ( lp_yy_cp < YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf + 2 ) + YY_FATAL_ERROR( "flex scanner push-back overflow" ); + } + + *--lp_yy_cp = (char) c; + + if ( c == '\n' ){ + --lp_yylineno; + } + + lp_yyg->lp_yytext_ptr = lp_yy_bp; + lp_yyg->lp_yy_hold_char = *lp_yy_cp; + lp_yyg->lp_yy_c_buf_p = lp_yy_cp; +} + +#ifndef YY_NO_INPUT +#ifdef __cplusplus + static inline int lp_yyinput (lp_yyscan_t lp_yyscanner) +#else + static inline int input (lp_yyscan_t lp_yyscanner) +#endif + +{ + int c; + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + *lp_yyg->lp_yy_c_buf_p = lp_yyg->lp_yy_hold_char; + + if ( *lp_yyg->lp_yy_c_buf_p == YY_END_OF_BUFFER_CHAR ) + { + /* lp_yy_c_buf_p now points to the character we want to return. + * If this occurs *before* the EOB characters, then it's a + * valid NUL; if not, then we've hit the end of the buffer. + */ + if ( lp_yyg->lp_yy_c_buf_p < &YY_CURRENT_BUFFER_LVALUE->lp_yy_ch_buf[lp_yyg->lp_yy_n_chars] ) + /* This was really a NUL. */ + *lp_yyg->lp_yy_c_buf_p = '\0'; + + else + { /* need more input */ + int offset = lp_yyg->lp_yy_c_buf_p - lp_yyg->lp_yytext_ptr; + ++lp_yyg->lp_yy_c_buf_p; + + switch ( lp_yy_get_next_buffer( lp_yyscanner ) ) + { + case EOB_ACT_LAST_MATCH: + /* This happens because lp_yy_g_n_b() + * sees that we've accumulated a + * token and flags that we need to + * try matching the token before + * proceeding. But for input(), + * there's no matching to consider. + * So convert the EOB_ACT_LAST_MATCH + * to EOB_ACT_END_OF_FILE. + */ + + /* Reset buffer status. */ + lp_yyrestart(lp_yyin ,lp_yyscanner); + + /*FALLTHROUGH*/ + + case EOB_ACT_END_OF_FILE: + { + if ( lp_yywrap(lp_yyscanner ) ) + return EOF; + + if ( ! lp_yyg->lp_yy_did_buffer_switch_on_eof ) + YY_NEW_FILE; +#ifdef __cplusplus + return lp_yyinput(lp_yyscanner); +#else + return input(lp_yyscanner); +#endif + } + + case EOB_ACT_CONTINUE_SCAN: + lp_yyg->lp_yy_c_buf_p = lp_yyg->lp_yytext_ptr + offset; + break; + } + } + } + + c = *(unsigned char *) lp_yyg->lp_yy_c_buf_p; /* cast for 8-bit char's */ + *lp_yyg->lp_yy_c_buf_p = '\0'; /* preserve lp_yytext */ + lp_yyg->lp_yy_hold_char = *++lp_yyg->lp_yy_c_buf_p; + + YY_CURRENT_BUFFER_LVALUE->lp_yy_at_bol = (c == '\n'); + if ( YY_CURRENT_BUFFER_LVALUE->lp_yy_at_bol ) + + do{ lp_yylineno++; + lp_yycolumn=0; + }while(0) +; + + return c; +} +#endif /* ifndef YY_NO_INPUT */ + +/** Immediately switch to a different input stream. + * @param input_file A readable stream. + * @param lp_yyscanner The scanner object. + * @note This function does not reset the start condition to @c INITIAL . + */ + void lp_yyrestart (FILE * input_file , lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + if ( ! YY_CURRENT_BUFFER ){ + lp_yyensure_buffer_stack (lp_yyscanner); + YY_CURRENT_BUFFER_LVALUE = + lp_yy_create_buffer(lp_yyin,YY_BUF_SIZE ,lp_yyscanner); + } + + lp_yy_init_buffer(YY_CURRENT_BUFFER,input_file ,lp_yyscanner); + lp_yy_load_buffer_state(lp_yyscanner ); +} + +/** Switch to a different input buffer. + * @param new_buffer The new input buffer. + * @param lp_yyscanner The scanner object. + */ + void lp_yy_switch_to_buffer (YY_BUFFER_STATE new_buffer , lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + /* TODO. We should be able to replace this entire function body + * with + * lp_yypop_buffer_state(); + * lp_yypush_buffer_state(new_buffer); + */ + lp_yyensure_buffer_stack (lp_yyscanner); + if ( YY_CURRENT_BUFFER == new_buffer ) + return; + + if ( YY_CURRENT_BUFFER ) + { + /* Flush out information for old buffer. */ + *lp_yyg->lp_yy_c_buf_p = lp_yyg->lp_yy_hold_char; + YY_CURRENT_BUFFER_LVALUE->lp_yy_buf_pos = lp_yyg->lp_yy_c_buf_p; + YY_CURRENT_BUFFER_LVALUE->lp_yy_n_chars = lp_yyg->lp_yy_n_chars; + } + + YY_CURRENT_BUFFER_LVALUE = new_buffer; + lp_yy_load_buffer_state(lp_yyscanner ); + + /* We don't actually know whether we did this switch during + * EOF (lp_yywrap()) processing, but the only time this flag + * is looked at is after lp_yywrap() is called, so it's safe + * to go ahead and always set it. + */ + lp_yyg->lp_yy_did_buffer_switch_on_eof = 1; +} + +static void lp_yy_load_buffer_state (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + lp_yyg->lp_yy_n_chars = YY_CURRENT_BUFFER_LVALUE->lp_yy_n_chars; + lp_yyg->lp_yytext_ptr = lp_yyg->lp_yy_c_buf_p = YY_CURRENT_BUFFER_LVALUE->lp_yy_buf_pos; + lp_yyin = YY_CURRENT_BUFFER_LVALUE->lp_yy_input_file; + lp_yyg->lp_yy_hold_char = *lp_yyg->lp_yy_c_buf_p; +} + +/** Allocate and initialize an input buffer state. + * @param file A readable stream. + * @param size The character buffer size in bytes. When in doubt, use @c YY_BUF_SIZE. + * @param lp_yyscanner The scanner object. + * @return the allocated buffer state. + */ + YY_BUFFER_STATE lp_yy_create_buffer (FILE * file, int size , lp_yyscan_t lp_yyscanner) +{ + YY_BUFFER_STATE b; + + b = (YY_BUFFER_STATE) lp_yyalloc(sizeof( struct lp_yy_buffer_state ) ,lp_yyscanner ); + if ( ! b ) + YY_FATAL_ERROR( "out of dynamic memory in lp_yy_create_buffer()" ); + + b->lp_yy_buf_size = size; + + /* lp_yy_ch_buf has to be 2 characters longer than the size given because + * we need to put in 2 end-of-buffer characters. + */ + b->lp_yy_ch_buf = (char *) lp_yyalloc(b->lp_yy_buf_size + 2 ,lp_yyscanner ); + if ( ! b->lp_yy_ch_buf ) + YY_FATAL_ERROR( "out of dynamic memory in lp_yy_create_buffer()" ); + + b->lp_yy_is_our_buffer = 1; + + lp_yy_init_buffer(b,file ,lp_yyscanner); + + return b; +} + +/** Destroy the buffer. + * @param b a buffer created with lp_yy_create_buffer() + * @param lp_yyscanner The scanner object. + */ + void lp_yy_delete_buffer (YY_BUFFER_STATE b , lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + if ( ! b ) + return; + + if ( b == YY_CURRENT_BUFFER ) /* Not sure if we should pop here. */ + YY_CURRENT_BUFFER_LVALUE = (YY_BUFFER_STATE) 0; + + if ( b->lp_yy_is_our_buffer ) + lp_yyfree((void *) b->lp_yy_ch_buf ,lp_yyscanner ); + + lp_yyfree((void *) b ,lp_yyscanner ); +} + +#ifndef __cplusplus +extern int isatty (int ); +#endif /* __cplusplus */ + +/* Initializes or reinitializes a buffer. + * This function is sometimes called more than once on the same buffer, + * such as during a lp_yyrestart() or at EOF. + */ + static void lp_yy_init_buffer (YY_BUFFER_STATE b, FILE * file , lp_yyscan_t lp_yyscanner) + +{ + int oerrno = errno; + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + lp_yy_flush_buffer(b ,lp_yyscanner); + + b->lp_yy_input_file = file; + b->lp_yy_fill_buffer = 1; + + /* If b is the current buffer, then lp_yy_init_buffer was _probably_ + * called from lp_yyrestart() or through lp_yy_get_next_buffer. + * In that case, we don't want to reset the lineno or column. + */ + if (b != YY_CURRENT_BUFFER){ + b->lp_yy_bs_lineno = 1; + b->lp_yy_bs_column = 0; + } + + b->lp_yy_is_interactive = file ? (isatty( fileno(file) ) > 0) : 0; + + errno = oerrno; +} + +/** Discard all buffered characters. On the next scan, YY_INPUT will be called. + * @param b the buffer state to be flushed, usually @c YY_CURRENT_BUFFER. + * @param lp_yyscanner The scanner object. + */ + void lp_yy_flush_buffer (YY_BUFFER_STATE b , lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + if ( ! b ) + return; + + b->lp_yy_n_chars = 0; + + /* We always need two end-of-buffer characters. The first causes + * a transition to the end-of-buffer state. The second causes + * a jam in that state. + */ + b->lp_yy_ch_buf[0] = YY_END_OF_BUFFER_CHAR; + b->lp_yy_ch_buf[1] = YY_END_OF_BUFFER_CHAR; + + b->lp_yy_buf_pos = &b->lp_yy_ch_buf[0]; + + b->lp_yy_at_bol = 1; + b->lp_yy_buffer_status = YY_BUFFER_NEW; + + if ( b == YY_CURRENT_BUFFER ) + lp_yy_load_buffer_state(lp_yyscanner ); +} + +/** Pushes the new state onto the stack. The new state becomes + * the current state. This function will allocate the stack + * if necessary. + * @param new_buffer The new state. + * @param lp_yyscanner The scanner object. + */ +void lp_yypush_buffer_state (YY_BUFFER_STATE new_buffer , lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + if (new_buffer == NULL) + return; + + lp_yyensure_buffer_stack(lp_yyscanner); + + /* This block is copied from lp_yy_switch_to_buffer. */ + if ( YY_CURRENT_BUFFER ) + { + /* Flush out information for old buffer. */ + *lp_yyg->lp_yy_c_buf_p = lp_yyg->lp_yy_hold_char; + YY_CURRENT_BUFFER_LVALUE->lp_yy_buf_pos = lp_yyg->lp_yy_c_buf_p; + YY_CURRENT_BUFFER_LVALUE->lp_yy_n_chars = lp_yyg->lp_yy_n_chars; + } + + /* Only push if top exists. Otherwise, replace top. */ + if (YY_CURRENT_BUFFER) + lp_yyg->lp_yy_buffer_stack_top++; + YY_CURRENT_BUFFER_LVALUE = new_buffer; + + /* copied from lp_yy_switch_to_buffer. */ + lp_yy_load_buffer_state(lp_yyscanner ); + lp_yyg->lp_yy_did_buffer_switch_on_eof = 1; +} + +/** Removes and deletes the top of the stack, if present. + * The next element becomes the new top. + * @param lp_yyscanner The scanner object. + */ +void lp_yypop_buffer_state (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + if (!YY_CURRENT_BUFFER) + return; + + lp_yy_delete_buffer(YY_CURRENT_BUFFER ,lp_yyscanner); + YY_CURRENT_BUFFER_LVALUE = NULL; + if (lp_yyg->lp_yy_buffer_stack_top > 0) + --lp_yyg->lp_yy_buffer_stack_top; + + if (YY_CURRENT_BUFFER) { + lp_yy_load_buffer_state(lp_yyscanner ); + lp_yyg->lp_yy_did_buffer_switch_on_eof = 1; + } +} + +/* Allocates the stack if it does not exist. + * Guarantees space for at least one push. + */ +static void lp_yyensure_buffer_stack (lp_yyscan_t lp_yyscanner) +{ + int num_to_alloc; + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + if (!lp_yyg->lp_yy_buffer_stack) { + + /* First allocation is just for 2 elements, since we don't know if this + * scanner will even need a stack. We use 2 instead of 1 to avoid an + * immediate realloc on the next call. + */ + num_to_alloc = 1; + lp_yyg->lp_yy_buffer_stack = (struct lp_yy_buffer_state**)lp_yyalloc + (num_to_alloc * sizeof(struct lp_yy_buffer_state*) + , lp_yyscanner); + if ( ! lp_yyg->lp_yy_buffer_stack ) + YY_FATAL_ERROR( "out of dynamic memory in lp_yyensure_buffer_stack()" ); + + memset(lp_yyg->lp_yy_buffer_stack, 0, num_to_alloc * sizeof(struct lp_yy_buffer_state*)); + + lp_yyg->lp_yy_buffer_stack_max = num_to_alloc; + lp_yyg->lp_yy_buffer_stack_top = 0; + return; + } + + if (lp_yyg->lp_yy_buffer_stack_top >= (lp_yyg->lp_yy_buffer_stack_max) - 1){ + + /* Increase the buffer to prepare for a possible push. */ + int grow_size = 8 /* arbitrary grow size */; + + num_to_alloc = lp_yyg->lp_yy_buffer_stack_max + grow_size; + lp_yyg->lp_yy_buffer_stack = (struct lp_yy_buffer_state**)lp_yyrealloc + (lp_yyg->lp_yy_buffer_stack, + num_to_alloc * sizeof(struct lp_yy_buffer_state*) + , lp_yyscanner); + if ( ! lp_yyg->lp_yy_buffer_stack ) + YY_FATAL_ERROR( "out of dynamic memory in lp_yyensure_buffer_stack()" ); + + /* zero only the new slots.*/ + memset(lp_yyg->lp_yy_buffer_stack + lp_yyg->lp_yy_buffer_stack_max, 0, grow_size * sizeof(struct lp_yy_buffer_state*)); + lp_yyg->lp_yy_buffer_stack_max = num_to_alloc; + } +} + +/** Setup the input buffer state to scan directly from a user-specified character buffer. + * @param base the character buffer + * @param size the size in bytes of the character buffer + * @param lp_yyscanner The scanner object. + * @return the newly allocated buffer state object. + */ +YY_BUFFER_STATE lp_yy_scan_buffer (char * base, lp_yy_size_t size , lp_yyscan_t lp_yyscanner) +{ + YY_BUFFER_STATE b; + + if ( size < 2 || + base[size-2] != YY_END_OF_BUFFER_CHAR || + base[size-1] != YY_END_OF_BUFFER_CHAR ) + /* They forgot to leave room for the EOB's. */ + return 0; + + b = (YY_BUFFER_STATE) lp_yyalloc(sizeof( struct lp_yy_buffer_state ) ,lp_yyscanner ); + if ( ! b ) + YY_FATAL_ERROR( "out of dynamic memory in lp_yy_scan_buffer()" ); + + b->lp_yy_buf_size = size - 2; /* "- 2" to take care of EOB's */ + b->lp_yy_buf_pos = b->lp_yy_ch_buf = base; + b->lp_yy_is_our_buffer = 0; + b->lp_yy_input_file = 0; + b->lp_yy_n_chars = b->lp_yy_buf_size; + b->lp_yy_is_interactive = 0; + b->lp_yy_at_bol = 1; + b->lp_yy_fill_buffer = 0; + b->lp_yy_buffer_status = YY_BUFFER_NEW; + + lp_yy_switch_to_buffer(b ,lp_yyscanner ); + + return b; +} + +/** Setup the input buffer state to scan a string. The next call to lp_yylex() will + * scan from a @e copy of @a str. + * @param lp_yystr a NUL-terminated string to scan + * @param lp_yyscanner The scanner object. + * @return the newly allocated buffer state object. + * @note If you want to scan bytes that may contain NUL values, then use + * lp_yy_scan_bytes() instead. + */ +YY_BUFFER_STATE lp_yy_scan_string (lp_yyconst char * lp_yystr , lp_yyscan_t lp_yyscanner) +{ + + return lp_yy_scan_bytes(lp_yystr,strlen(lp_yystr) ,lp_yyscanner); +} + +/** Setup the input buffer state to scan the given bytes. The next call to lp_yylex() will + * scan from a @e copy of @a bytes. + * @param bytes the byte buffer to scan + * @param len the number of bytes in the buffer pointed to by @a bytes. + * @param lp_yyscanner The scanner object. + * @return the newly allocated buffer state object. + */ +YY_BUFFER_STATE lp_yy_scan_bytes (lp_yyconst char * lp_yybytes, int _lp_yybytes_len , lp_yyscan_t lp_yyscanner) +{ + YY_BUFFER_STATE b; + char *buf; + lp_yy_size_t n; + int i; + + /* Get memory for full buffer, including space for trailing EOB's. */ + n = _lp_yybytes_len + 2; + buf = (char *) lp_yyalloc(n ,lp_yyscanner ); + if ( ! buf ) + YY_FATAL_ERROR( "out of dynamic memory in lp_yy_scan_bytes()" ); + + for ( i = 0; i < _lp_yybytes_len; ++i ) + buf[i] = lp_yybytes[i]; + + buf[_lp_yybytes_len] = buf[_lp_yybytes_len+1] = YY_END_OF_BUFFER_CHAR; + + b = lp_yy_scan_buffer(buf,n ,lp_yyscanner); + if ( ! b ) + YY_FATAL_ERROR( "bad buffer in lp_yy_scan_bytes()" ); + + /* It's okay to grow etc. this buffer, and we should throw it + * away when we're done. + */ + b->lp_yy_is_our_buffer = 1; + + return b; +} + +#ifndef YY_EXIT_FAILURE +#define YY_EXIT_FAILURE 2 +#endif + +static void lp_yy_fatal_error (lp_yyconst char* msg , lp_yyscan_t lp_yyscanner) +{ + (void) fprintf( stderr, "%s\n", msg ); + exit( YY_EXIT_FAILURE ); +} + +/* Redefine lp_yyless() so it works in section 3 code. */ + +#undef lp_yyless +#define lp_yyless(n) \ + do \ + { \ + /* Undo effects of setting up lp_yytext. */ \ + int lp_yyless_macro_arg = (n); \ + YY_LESS_LINENO(lp_yyless_macro_arg);\ + lp_yytext[lp_yyleng] = lp_yyg->lp_yy_hold_char; \ + lp_yyg->lp_yy_c_buf_p = lp_yytext + lp_yyless_macro_arg; \ + lp_yyg->lp_yy_hold_char = *lp_yyg->lp_yy_c_buf_p; \ + *lp_yyg->lp_yy_c_buf_p = '\0'; \ + lp_yyleng = lp_yyless_macro_arg; \ + } \ + while ( 0 ) + +/* Accessor methods (get/set functions) to struct members. */ + +/** Get the user-defined data for this scanner. + * @param lp_yyscanner The scanner object. + */ +YY_EXTRA_TYPE lp_yyget_extra (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + return lp_yyextra; +} + +/** Get the current line number. + * @param lp_yyscanner The scanner object. + */ +int lp_yyget_lineno (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + if (! YY_CURRENT_BUFFER) + return 0; + + return lp_yylineno; +} + +/** Get the current column number. + * @param lp_yyscanner The scanner object. + */ +int lp_yyget_column (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + if (! YY_CURRENT_BUFFER) + return 0; + + return lp_yycolumn; +} + +/** Get the input stream. + * @param lp_yyscanner The scanner object. + */ +FILE *lp_yyget_in (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + return lp_yyin; +} + +/** Get the output stream. + * @param lp_yyscanner The scanner object. + */ +FILE *lp_yyget_out (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + return lp_yyout; +} + +/** Get the length of the current token. + * @param lp_yyscanner The scanner object. + */ +int lp_yyget_leng (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + return lp_yyleng; +} + +/** Get the current token. + * @param lp_yyscanner The scanner object. + */ + +char *lp_yyget_text (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + return lp_yytext; +} + +/** Set the user-defined data. This data is never touched by the scanner. + * @param user_defined The data to be associated with this scanner. + * @param lp_yyscanner The scanner object. + */ +void lp_yyset_extra (YY_EXTRA_TYPE user_defined , lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + lp_yyextra = user_defined ; +} + +/** Set the current line number. + * @param line_number + * @param lp_yyscanner The scanner object. + */ +void lp_yyset_lineno (int line_number , lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + /* lineno is only valid if an input buffer exists. */ + if (! YY_CURRENT_BUFFER ) + lp_yy_fatal_error( "lp_yyset_lineno called with no buffer" , lp_yyscanner); + + lp_yylineno = line_number; +} + +/** Set the current column. + * @param line_number + * @param lp_yyscanner The scanner object. + */ +void lp_yyset_column (int column_no , lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + /* column is only valid if an input buffer exists. */ + if (! YY_CURRENT_BUFFER ) + lp_yy_fatal_error( "lp_yyset_column called with no buffer" , lp_yyscanner); + + lp_yycolumn = column_no; +} + +/** Set the input stream. This does not discard the current + * input buffer. + * @param in_str A readable stream. + * @param lp_yyscanner The scanner object. + * @see lp_yy_switch_to_buffer + */ +void lp_yyset_in (FILE * in_str , lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + lp_yyin = in_str ; +} + +void lp_yyset_out (FILE * out_str , lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + lp_yyout = out_str ; +} + +int lp_yyget_debug (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + return lp_yy_flex_debug; +} + +void lp_yyset_debug (int bdebug , lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + lp_yy_flex_debug = bdebug ; +} + +/* Accessor methods for lp_yylval and lp_yylloc */ + +YYSTYPE * lp_yyget_lval (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + return lp_yylval; +} + +void lp_yyset_lval (YYSTYPE * lp_yylval_param , lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + lp_yylval = lp_yylval_param; +} + +/* User-visible API */ + +/* lp_yylex_init is special because it creates the scanner itself, so it is + * the ONLY reentrant function that doesn't take the scanner as the last argument. + * That's why we explicitly handle the declaration, instead of using our macros. + */ + +int lp_yylex_init(lp_yyscan_t* ptr_lp_yy_globals) + +{ + if (ptr_lp_yy_globals == NULL){ + errno = EINVAL; + return 1; + } + + *ptr_lp_yy_globals = (lp_yyscan_t) lp_yyalloc ( sizeof( struct lp_yyguts_t ), NULL ); + + if (*ptr_lp_yy_globals == NULL){ + errno = ENOMEM; + return 1; + } + + /* By setting to 0xAA, we expose bugs in lp_yy_init_globals. Leave at 0x00 for releases. */ + memset(*ptr_lp_yy_globals,0x00,sizeof(struct lp_yyguts_t)); + + return lp_yy_init_globals ( *ptr_lp_yy_globals ); +} + +/* lp_yylex_init_extra has the same functionality as lp_yylex_init, but follows the + * convention of taking the scanner as the last argument. Note however, that + * this is a *pointer* to a scanner, as it will be allocated by this call (and + * is the reason, too, why this function also must handle its own declaration). + * The user defined value in the first argument will be available to lp_yyalloc in + * the lp_yyextra field. + */ + +int lp_yylex_init_extra(YY_EXTRA_TYPE lp_yy_user_defined,lp_yyscan_t* ptr_lp_yy_globals ) + +{ + struct lp_yyguts_t dummy_lp_yyguts; + + lp_yyset_extra (lp_yy_user_defined, &dummy_lp_yyguts); + + if (ptr_lp_yy_globals == NULL){ + errno = EINVAL; + return 1; + } + + *ptr_lp_yy_globals = (lp_yyscan_t) lp_yyalloc ( sizeof( struct lp_yyguts_t ), &dummy_lp_yyguts ); + + if (*ptr_lp_yy_globals == NULL){ + errno = ENOMEM; + return 1; + } + + /* By setting to 0xAA, we expose bugs in + lp_yy_init_globals. Leave at 0x00 for releases. */ + memset(*ptr_lp_yy_globals,0x00,sizeof(struct lp_yyguts_t)); + + lp_yyset_extra (lp_yy_user_defined, *ptr_lp_yy_globals); + + return lp_yy_init_globals ( *ptr_lp_yy_globals ); +} + +static int lp_yy_init_globals (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + /* Initialization is the same as for the non-reentrant scanner. + * This function is called from lp_yylex_destroy(), so don't allocate here. + */ + + lp_yyg->lp_yy_buffer_stack = 0; + lp_yyg->lp_yy_buffer_stack_top = 0; + lp_yyg->lp_yy_buffer_stack_max = 0; + lp_yyg->lp_yy_c_buf_p = (char *) 0; + lp_yyg->lp_yy_init = 0; + lp_yyg->lp_yy_start = 0; + + lp_yyg->lp_yy_start_stack_ptr = 0; + lp_yyg->lp_yy_start_stack_depth = 0; + lp_yyg->lp_yy_start_stack = NULL; + +/* Defined in main.c */ +#ifdef YY_STDINIT + lp_yyin = stdin; + lp_yyout = stdout; +#else + lp_yyin = (FILE *) 0; + lp_yyout = (FILE *) 0; +#endif + + /* For future reference: Set errno on error, since we are called by + * lp_yylex_init() + */ + return 0; +} + +/* lp_yylex_destroy is for both reentrant and non-reentrant scanners. */ +int lp_yylex_destroy (lp_yyscan_t lp_yyscanner) +{ + struct lp_yyguts_t * lp_yyg = (struct lp_yyguts_t*)lp_yyscanner; + + /* Pop the buffer stack, destroying each element. */ + while(YY_CURRENT_BUFFER){ + lp_yy_delete_buffer(YY_CURRENT_BUFFER ,lp_yyscanner ); + YY_CURRENT_BUFFER_LVALUE = NULL; + lp_yypop_buffer_state(lp_yyscanner); + } + + /* Destroy the stack itself. */ + lp_yyfree(lp_yyg->lp_yy_buffer_stack ,lp_yyscanner); + lp_yyg->lp_yy_buffer_stack = NULL; + + /* Destroy the start condition stack. */ + lp_yyfree(lp_yyg->lp_yy_start_stack ,lp_yyscanner ); + lp_yyg->lp_yy_start_stack = NULL; + + /* Reset the globals. This is important in a non-reentrant scanner so the next time + * lp_yylex() is called, initialization will occur. */ + lp_yy_init_globals( lp_yyscanner); + + /* Destroy the main struct (reentrant only). */ + lp_yyfree ( lp_yyscanner , lp_yyscanner ); + lp_yyscanner = NULL; + return 0; +} + +/* + * Internal utility routines. + */ + +#ifndef lp_yytext_ptr +static void lp_yy_flex_strncpy (char* s1, lp_yyconst char * s2, int n , lp_yyscan_t lp_yyscanner) +{ + register int i; + for ( i = 0; i < n; ++i ) + s1[i] = s2[i]; +} +#endif + +#ifdef YY_NEED_STRLEN +static int lp_yy_flex_strlen (lp_yyconst char * s , lp_yyscan_t lp_yyscanner) +{ + register int n; + for ( n = 0; s[n]; ++n ) + ; + + return n; +} +#endif + +void *lp_yyalloc (lp_yy_size_t size , lp_yyscan_t lp_yyscanner) +{ + return (void *) malloc( size ); +} + +void *lp_yyrealloc (void * ptr, lp_yy_size_t size , lp_yyscan_t lp_yyscanner) +{ + /* The cast to (char *) in the following accommodates both + * implementations that use char* generic pointers, and those + * that use void* generic pointers. It works with the latter + * because both ANSI C and C++ allow castless assignment from + * any pointer type to void*, and deal with argument conversions + * as though doing an assignment. + */ + return (void *) realloc( (char *) ptr, size ); +} + +void lp_yyfree (void * ptr , lp_yyscan_t lp_yyscanner) +{ + free( (char *) ptr ); /* see lp_yyrealloc() for (char *) cast */ +} + +#define YYTABLES_NAME "lp_yytables" + From 074a562ab1984fcd1f3aa58f6bd63d0d109842ca Mon Sep 17 00:00:00 2001 From: Apostolos Chalkis Date: Tue, 17 Oct 2023 07:00:09 -0600 Subject: [PATCH 02/35] No-U-Turn Sampler in HMC (#216) * initialize nuts sampler class * implement the burnin of nuts sampler * add tests and resolve bugs * implement e0 estimation in burnin of nuts * optimize leapfrog * integrate nuts into the R interface * document random walk in sample_points.cpp in R interface * fix burnin for the non-truncated case * resolve bugs in hmc and nuts pipelines * improve the preprocess in burin step of nuts * split large lines in sample_points.cpp * Add NUTS C++ example and update CMake (#29) * resolve PR comments * fix minor bug * fix compiler bug * fix error in building the C++ examples * resolve warnings in sample_points * fix lpsolve cran warning * fix cran warning on mac * improve lpsolve cmake for cran check * fix R warning in mac test * remove lpsolve header * resolve PR comments --------- Co-authored-by: Marios Papachristou Co-authored-by: Apostolos Chalkis --- R-proj/R/RcppExports.R | 5 - R-proj/examples/logconcave/nuts_rand_poly.R | 55 +++ .../logconcave/simple_hmc_rand_poly.R | 4 +- R-proj/man/sample_points.Rd | 18 +- R-proj/src/sample_points.cpp | 54 ++- examples/logconcave/CMakeLists.txt | 18 +- external/cmake-files/LPSolve.cmake | 2 +- include/cartesian_geom/point.h | 1 + include/ode_solvers/leapfrog.hpp | 39 +- .../estimate_L_smooth_parameter.hpp | 73 ++++ .../hamiltonian_monte_carlo_walk.hpp | 9 +- include/random_walks/nuts_hmc_walk.hpp | 380 ++++++++++++++++++ include/random_walks/random_walks.hpp | 1 + include/sampling/random_point_generators.hpp | 19 +- include/sampling/sampling.hpp | 38 +- test/CMakeLists.txt | 5 + test/logconcave_sampling_test.cpp | 89 ++++ 17 files changed, 732 insertions(+), 78 deletions(-) create mode 100644 R-proj/examples/logconcave/nuts_rand_poly.R create mode 100644 include/preprocess/estimate_L_smooth_parameter.hpp create mode 100644 include/random_walks/nuts_hmc_walk.hpp diff --git a/R-proj/R/RcppExports.R b/R-proj/R/RcppExports.R index 5713d11a3..c3f192aee 100644 --- a/R-proj/R/RcppExports.R +++ b/R-proj/R/RcppExports.R @@ -361,11 +361,6 @@ sample_points <- function(P, n, random_walk = NULL, distribution = NULL, seed = .Call(`_volesti_sample_points`, P, n, random_walk, distribution, seed) } -#' @export -sample_spectra <- function(file = NULL, N = NULL, walk_length = NULL) { - .Call(`_volesti_sample_spectra`, file, N, walk_length) -} - #' Write a SDPA format file #' #' Outputs a spectrahedron (the matrices defining a linear matrix inequality) and a vector (the objective function) diff --git a/R-proj/examples/logconcave/nuts_rand_poly.R b/R-proj/examples/logconcave/nuts_rand_poly.R new file mode 100644 index 000000000..00400ba72 --- /dev/null +++ b/R-proj/examples/logconcave/nuts_rand_poly.R @@ -0,0 +1,55 @@ +# VolEsti (volume computation and sampling library) + +# Copyright (c) 2012-2020 Vissarion Fisikopoulos +# Copyright (c) 2018-2020 Apostolos Chalkis +# Copyright (c) 2020-2020 Marios Papachristou + +# Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2020 program. + +# Licensed under GNU LGPL.3, see LICENCE file + +# Example script for using the logconcave sampling methods + +# Import required libraries +library(ggplot2) +library(volesti) + +# Sampling from logconcave density example + +# Helper function +norm_vec <- function(x) sqrt(sum(x^2)) + +# Negative log-probability oracle +f <- function(x) (norm_vec(x)^2 + sum(x)) + +# Negative log-probability gradient oracle +grad_f <- function(x) (2 * x + 1) + +dimension <- 50 +facets <- 200 + +# Create domain of truncation +H <- gen_rand_hpoly(dimension, facets, seed = 15) + +# Rounding +Tr <- rounding(H, seed = 127) + +P <- Hpolytope$new(A = Tr$Mat[1:nrow(Tr$Mat), 2:ncol(Tr$Mat)], b = Tr$Mat[,1]) + +x_min = matrix(0, dimension, 1) + +# Warm start point from truncated Gaussian +warm_start <- sample_points(P, n = 1, random_walk = list("nburns" = 5000), distribution = list("density" = "gaussian", "variance" = 1/2, "mode" = x_min)) + +# Sample points +n_samples <- 20000 + +samples <- sample_points(P, n = n_samples, random_walk = list("walk" = "NUTS", "solver" = "leapfrog", "starting_point" = warm_start[,1]), + distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f)) + +# Plot histogram +hist(samples[1,], probability=TRUE, breaks = 100) + +psrfs <- psrf_univariate(samples) +n_ess <- ess(samples) + diff --git a/R-proj/examples/logconcave/simple_hmc_rand_poly.R b/R-proj/examples/logconcave/simple_hmc_rand_poly.R index 9f785aaa8..b352c9a16 100644 --- a/R-proj/examples/logconcave/simple_hmc_rand_poly.R +++ b/R-proj/examples/logconcave/simple_hmc_rand_poly.R @@ -29,10 +29,10 @@ dimension <- 50 facets <- 200 # Create domain of truncation -H <- gen_rand_hpoly(dimension, facets) +H <- gen_rand_hpoly(dimension, facets, seed = 15) # Rounding -Tr <- rounding(H) +Tr <- rounding(H, seed = 127) P <- Hpolytope$new(A = Tr$Mat[1:nrow(Tr$Mat), 2:ncol(Tr$Mat)], b = Tr$Mat[,1]) diff --git a/R-proj/man/sample_points.Rd b/R-proj/man/sample_points.Rd index f5ea6f2bf..ca2ef87fa 100644 --- a/R-proj/man/sample_points.Rd +++ b/R-proj/man/sample_points.Rd @@ -13,7 +13,7 @@ sample_points(P, n, random_walk = NULL, distribution = NULL, seed = NULL) \item{random_walk}{Optional. A list that declares the random walk and some related parameters as follows: \itemize{ -\item{\code{walk} }{ A string to declare the random walk: i) \code{'CDHR'} for Coordinate Directions Hit-and-Run, ii) \code{'RDHR'} for Random Directions Hit-and-Run, iii) \code{'BaW'} for Ball Walk, iv) \code{'BiW'} for Billiard walk, v) \code{'dikin'} for dikin walk, vi) \code{'vaidya'} for vaidya walk, vii) \code{'john'} for john walk, viii) \code{'BCDHR'} boundary sampling by keeping the extreme points of CDHR or ix) \code{'BRDHR'} boundary sampling by keeping the extreme points of RDHR x) \code{'HMC'} for Hamiltonian Monte Carlo (logconcave) xi) \code{'ULD'} for Underdamped Langevin Dynamics using the Randomized Midpoint Method. The default walk is \code{'aBiW'} for the uniform distribution or \code{'CDHR'} for the Gaussian distribution and H-polytopes and \code{'BiW'} or \code{'RDHR'} for the same distributions and V-polytopes and zonotopes.} +\item{\code{walk} }{ A string to declare the random walk: i) \code{'CDHR'} for Coordinate Directions Hit-and-Run, ii) \code{'RDHR'} for Random Directions Hit-and-Run, iii) \code{'BaW'} for Ball Walk, iv) \code{'BiW'} for Billiard walk, v) \code{'dikin'} for dikin walk, vi) \code{'vaidya'} for vaidya walk, vii) \code{'john'} for john walk, viii) \code{'BCDHR'} boundary sampling by keeping the extreme points of CDHR or ix) \code{'BRDHR'} boundary sampling by keeping the extreme points of RDHR x) \code{'NUTS'} for NUTS Hamiltonian Monte Carlo sampler (logconcave densities) xi) \code{'HMC'} for Hamiltonian Monte Carlo (logconcave densities) xii) \code{'ULD'} for Underdamped Langevin Dynamics using the Randomized Midpoint Method (logconcave densities) xiii) \code{'ExactHMC'} for exact Hamiltonian Monte Carlo with reflections (spherical Gaussian or exponential distribution). The default walk is \code{'aBiW'} for the uniform distribution, \code{'CDHR'} for the Gaussian distribution and H-polytopes and \code{'BiW'} or \code{'RDHR'} for the same distributions and V-polytopes and zonotopes. \code{'NUTS'} is the default sampler for logconcave densities.} \item{\code{walk_length} }{ The number of the steps per generated point for the random walk. The default value is \eqn{1}.} \item{\code{nburns} }{ The number of points to burn before start sampling. The default value is \eqn{1}.} \item{\code{starting_point} }{ A \eqn{d}-dimensional numerical vector that declares a starting point in the interior of the polytope for the random walk. The default choice is the center of the ball as that one computed by the function \code{inner_ball()}.} @@ -25,13 +25,14 @@ sample_points(P, n, random_walk = NULL, distribution = NULL, seed = NULL) \item{distribution}{Optional. A list that declares the target density and some related parameters as follows: \itemize{ -\item{\code{density} }{ A string: (a) \code{'uniform'} for the uniform distribution or b) \code{'gaussian'} for the multidimensional spherical distribution. The default target distribution is uniform. c) Logconcave with form proportional to exp(-f(x)) where f(x) is L-smooth and m-strongly-convex. } -\item{\code{variance} }{ The variance of the multidimensional spherical gaussian. The default value is 1.} +\item{\code{density} }{ A string: (a) \code{'uniform'} for the uniform distribution or b) \code{'gaussian'} for the multidimensional spherical distribution c) \code{logconcave} with form proportional to exp(-f(x)) where f(x) is L-smooth and m-strongly-convex d) \code{'exponential'} for the exponential distribution. The default target distribution is the uniform distribution.} +\item{\code{variance} }{ The variance of the multidimensional spherical gaussian or the exponential distribution. The default value is 1.} \item{\code{mode} }{ A \eqn{d}-dimensional numerical vector that declares the mode of the Gaussian distribution. The default choice is the center of the as that one computed by the function \code{inner_ball()}.} -\item{\code{L_}} { Smoothness constant (for logconcave). } -\item{\code{m}} { Strong-convexity constant (for logconcave). } -\item{\code{negative_logprob}} { Negative log-probability (for logconcave). } -\item{\code{negative_logprob_gradient}} { Negative log-probability gradient (for logconcave). } +\item{\code{bias} }{ The bias vector for the exponential distribution. The default vector is \eqn{c_1 = 1} and \eqn{c_i = 0} for \eqn{i \neq 1}.} +\item{\code{L_} }{ Smoothness constant (for logconcave). } +\item{\code{m} }{ Strong-convexity constant (for logconcave). } +\item{\code{negative_logprob} }{ Negative log-probability (for logconcave). } +\item{\code{negative_logprob_gradient} }{ Negative log-probability gradient (for logconcave). } }} \item{seed}{Optional. A fixed seed for the number generator.} @@ -75,4 +76,7 @@ points = sample_points(P, n = 100, random_walk = list("walk" = "BRDHR")) \cite{Shen, Ruoqi, and Yin Tat Lee, \dQuote{"The randomized midpoint method for log-concave sampling.",} \emph{Advances in Neural Information Processing Systems}, 2019.} + +\cite{Augustin Chevallier, Sylvain Pion, Frederic Cazals, +\dQuote{"Hamiltonian Monte Carlo with boundary reflections, and application to polytope volume calculations,"} \emph{Research Report preprint hal-01919855}, 2018.} } diff --git a/R-proj/src/sample_points.cpp b/R-proj/src/sample_points.cpp index b1150877f..4756d0915 100644 --- a/R-proj/src/sample_points.cpp +++ b/R-proj/src/sample_points.cpp @@ -36,6 +36,7 @@ enum random_walks { brdhr, bcdhr, hmc, + nuts, gaussian_hmc, exponential_hmc, uld @@ -213,6 +214,26 @@ void sample_from_polytope(Polytope &P, int type, RNGType &rng, PointList &randPo } break; + case nuts: + + logconcave_sampling < + PointList, + Polytope, + RNGType, + NutsHamiltonianMonteCarloWalk, + NT, + Point, + NegativeGradientFunctor, + NegativeLogprobFunctor, + LeapfrogODESolver < + Point, + NT, + Polytope, + NegativeGradientFunctor + > + >(randPoints, P, rng, walkL, numpoints, StartingPoint, nburns, *F, *f); + + break; case uld: logconcave_sampling < @@ -244,7 +265,16 @@ void sample_from_polytope(Polytope &P, int type, RNGType &rng, PointList &randPo //' @param n The number of points that the function is going to sample from the convex polytope. //' @param random_walk Optional. A list that declares the random walk and some related parameters as follows: //' \itemize{ -//' \item{\code{walk} }{ A string to declare the random walk: i) \code{'CDHR'} for Coordinate Directions Hit-and-Run, ii) \code{'RDHR'} for Random Directions Hit-and-Run, iii) \code{'BaW'} for Ball Walk, iv) \code{'BiW'} for Billiard walk, v) \code{'dikin'} for dikin walk, vi) \code{'vaidya'} for vaidya walk, vii) \code{'john'} for john walk, viii) \code{'BCDHR'} boundary sampling by keeping the extreme points of CDHR or ix) \code{'BRDHR'} boundary sampling by keeping the extreme points of RDHR x) \code{'HMC'} for Hamiltonian Monte Carlo (logconcave densities) xi) \code{'ULD'} for Underdamped Langevin Dynamics using the Randomized Midpoint Method xii) \code{'ExactHMC'} for exact Hamiltonian Monte Carlo with reflections (spherical Gaussian or exponential distribution). The default walk is \code{'aBiW'} for the uniform distribution or \code{'CDHR'} for the Gaussian distribution and H-polytopes and \code{'BiW'} or \code{'RDHR'} for the same distributions and V-polytopes and zonotopes.} +//' \item{\code{walk} }{ A string to declare the random walk: i) \code{'CDHR'} for Coordinate Directions Hit-and-Run, +//' ii) \code{'RDHR'} for Random Directions Hit-and-Run, iii) \code{'BaW'} for Ball Walk, +//' iv) \code{'BiW'} for Billiard walk, v) \code{'dikin'} for dikin walk, vi) \code{'vaidya'} for vaidya walk, +//' vii) \code{'john'} for john walk, viii) \code{'BCDHR'} boundary sampling by keeping the extreme points of CDHR or +//' ix) \code{'BRDHR'} boundary sampling by keeping the extreme points of RDHR x) \code{'NUTS'} for +//' NUTS Hamiltonian Monte Carlo sampler (logconcave densities) xi) \code{'HMC'} for Hamiltonian Monte Carlo (logconcave densities) +//' xii) \code{'ULD'} for Underdamped Langevin Dynamics using the Randomized Midpoint Method (logconcave densities) +//' xiii) \code{'ExactHMC'} for exact Hamiltonian Monte Carlo with reflections (spherical Gaussian or exponential distribution). +//' The default walk is \code{'aBiW'} for the uniform distribution, \code{'CDHR'} for the Gaussian distribution and H-polytopes +//' and \code{'BiW'} or \code{'RDHR'} for the same distributions and V-polytopes and zonotopes. \code{'NUTS'} is the default sampler for logconcave densities.} //' \item{\code{walk_length} }{ The number of the steps per generated point for the random walk. The default value is \eqn{1}.} //' \item{\code{nburns} }{ The number of points to burn before start sampling. The default value is \eqn{1}.} //' \item{\code{starting_point} }{ A \eqn{d}-dimensional numerical vector that declares a starting point in the interior of the polytope for the random walk. The default choice is the center of the ball as that one computed by the function \code{inner_ball()}.} @@ -410,18 +440,24 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, Rcpp::Function negative_logprob = Rcpp::as(distribution)["negative_logprob"]; Rcpp::Function negative_logprob_gradient = Rcpp::as(distribution)["negative_logprob_gradient"]; - NT L_, m; + NT L_ = 1, m = 1; if (Rcpp::as(distribution).containsElementNamed("L_")) { L_ = Rcpp::as(Rcpp::as(distribution)["L_"]); + if (L_ <= NT(0)) { + throw Rcpp::exception("The smoothness constant must be positive"); + } } else { - throw Rcpp::exception("The smoothness constant is absent"); + L_ = -1; } if (Rcpp::as(distribution).containsElementNamed("m")) { m = Rcpp::as(Rcpp::as(distribution)["m"]); + if (m <= NT(0)) { + throw Rcpp::exception("The strong-convexity constant must be positive"); + } } else { - throw Rcpp::exception("The strong-convexity constant is absent"); + m = -1; } if (Rcpp::as(random_walk).containsElementNamed("step_size")) { @@ -443,7 +479,6 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, throw Rcpp::exception("Invalid ODE solver specified. Aborting."); } } else { - Rcpp::warning("Solver set to leapfrog."); solver = leapfrog; } @@ -495,6 +530,8 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, throw Rcpp::exception("Exponential sampling is supported only for H-polytopes"); } walk = exponential_hmc; + } else if (logconcave) { + walk = nuts; } else if (gaussian) { if (type == 1) { walk = cdhr; @@ -571,7 +608,12 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, } } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("HMC")) == 0) { if (!logconcave) throw Rcpp::exception("HMC is not supported for non first-order sampling"); + if (F->params.L < 0) throw Rcpp::exception("The smoothness constant is absent"); + if (F->params.m < 0) throw Rcpp::exception("The strong-convexity constant is absent"); walk = hmc; + } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("NUTS")) == 0) { + if (!logconcave) throw Rcpp::exception("NUTS is not supported for non first-order sampling"); + walk = nuts; } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("ULD")) == 0) { if (!logconcave) throw Rcpp::exception("ULD is not supported for non first-order sampling"); walk = uld; @@ -703,7 +745,7 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, if (numpoints % 2 == 1 && (walk == brdhr || walk == bcdhr)) numpoints--; MT RetMat(dim, numpoints); unsigned int jj = 0; - + for (typename std::list::iterator rpit = randPoints.begin(); rpit!=randPoints.end(); rpit++, jj++) { if (gaussian) { RetMat.col(jj) = (*rpit).getCoefficients() + mode.getCoefficients(); diff --git a/examples/logconcave/CMakeLists.txt b/examples/logconcave/CMakeLists.txt index 0448ce3e3..33725d0c9 100644 --- a/examples/logconcave/CMakeLists.txt +++ b/examples/logconcave/CMakeLists.txt @@ -20,7 +20,7 @@ endif(COMMAND cmake_policy) option(DISABLE_NLP_ORACLES "Disable non-linear oracles (used in collocation)" ON) option(BUILTIN_EIGEN "Use eigen from ../external" OFF) -option(USE_MKL "Use MKL library to build eigen" ON) +option(USE_MKL "Use MKL library to build eigen" OFF) if(DISABLE_NLP_ORACLES) add_definitions(-DDISABLE_NLP_ORACLES) @@ -73,16 +73,24 @@ else () include_directories(BEFORE /usr/include/eigen3) endif(BUILTIN_EIGEN) +find_library(BLAS NAMES libblas.so libblas.dylib PATHS /usr/local/Cellar/lapack/3.9.1_1/lib /usr/lib/x86_64-linux-gnu /usr/lib/i386-linux-gnu /usr/local/Cellar/openblas/0.3.15_1/lib /usr/lib) + if (USE_MKL) - find_library(BLAS NAME libblas.so PATHS /usr/lib/x86_64-linux-gnu /usr/lib/i386-linux-gnu) + find_library(BLAS NAMES libblas.so libblas.dylib PATHS /usr/local/Cellar/lapack/3.9.1_1/lib /usr/lib/x86_64-linux-gnu /usr/lib/i386-linux-gnu /usr/local/Cellar/openblas/0.3.15_1/lib /usr/lib) + find_library(GFORTRAN NAME libgfortran.dylib PATHS /usr/local/Cellar/gcc/10.2.0_4/lib/gcc/10) + find_library(LAPACK NAME liblapack.dylib PATHS /usr/lib) + find_library(OPENMP NAME libiomp5.dylib PATHS /opt/intel/oneapi/compiler/2021.1.1/mac/compiler/lib) + include_directories (BEFORE ${MKLROOT}/include) - set(PROJECT_LIBS ${BLAS_LIBRARIES}) + set(PROJECT_LIBS ${BLAS_LIBRARIES} ${LAPACK_LIBRARIES} ${GFORTRAN_LIBRARIES}) set(MKL_LINK "-L${MKLROOT}/lib -Wl,-rpath,${MKLROOT}/lib -lmkl_intel_ilp64 -lmkl_sequential -lmkl_core -lpthread -lm -ldl") add_definitions(-DEIGEN_USE_MKL_ALL) +else() + set(MKL_LINK "") endif(USE_MKL) # Find lpsolve library -find_library(LP_SOLVE NAMES liblpsolve55.so PATHS /usr/lib/lp_solve) +find_library(LP_SOLVE NAMES liblpsolve55.so liblpsolve55.dylib PATHS /usr/lib/lp_solve /usr/local/lib) if (NOT LP_SOLVE) message(FATAL_ERROR "This program requires the lp_solve library, and will not be compiled.") @@ -145,6 +153,6 @@ else () TARGET_LINK_LIBRARIES(simple_hmc ${LP_SOLVE} ${BLAS} ${MKL_LINK} ) TARGET_LINK_LIBRARIES(exponential_exact_hmc ${LP_SOLVE} ${BLAS} ${MKL_LINK} ) TARGET_LINK_LIBRARIES(gaussian_exact_hmc ${LP_SOLVE} ${BLAS} ${MKL_LINK} ) - + endif() diff --git a/external/cmake-files/LPSolve.cmake b/external/cmake-files/LPSolve.cmake index b3f3cf98b..c5a173837 100644 --- a/external/cmake-files/LPSolve.cmake +++ b/external/cmake-files/LPSolve.cmake @@ -26,7 +26,7 @@ function(GetLPSolve) message(STATUS "lp_solve library found: ${LP_SOLVE_DIR}") endif() - + include_directories(${LP_SOLVE_DIR}) endfunction() diff --git a/include/cartesian_geom/point.h b/include/cartesian_geom/point.h index a2209f4f5..b8c8e32b4 100644 --- a/include/cartesian_geom/point.h +++ b/include/cartesian_geom/point.h @@ -81,6 +81,7 @@ class point void set_dimension(const unsigned int dim) { d = dim; + coeffs.setZero(d); } void set_coord(const unsigned int i, FT coord) diff --git a/include/ode_solvers/leapfrog.hpp b/include/ode_solvers/leapfrog.hpp index 4c1f299b0..b7f2724b4 100644 --- a/include/ode_solvers/leapfrog.hpp +++ b/include/ode_solvers/leapfrog.hpp @@ -56,6 +56,8 @@ struct LeapfrogODESolver { pts xs; pts xs_prev; + Point grad_x; + MT _AA; std::pair pbpair; @@ -69,11 +71,11 @@ struct LeapfrogODESolver { eta(step), eta0(step), t(initial_time), F(oracle), Ks(boundaries), xs(initial_state), adaptive(adaptive_) { dim = xs[0].dimension(); _update_parameters = update_parameters(); + grad_x.set_dimension(dim); initialize(); }; - void initialize() { for (unsigned int i = 0; i < xs.size(); i++) { VT ar, av; @@ -88,7 +90,6 @@ struct LeapfrogODESolver { Av.push_back(av); lambda_prev.push_back(NT(0)); } - //step(); } void disable_adaptive() { @@ -101,27 +102,28 @@ struct LeapfrogODESolver { void step(int k, bool accepted) { num_steps++; - if (adaptive) eta = (eta0 * num_steps) / (num_steps + num_reflections); xs_prev = xs; unsigned int x_index, v_index, it; t += eta; + Point y; for (unsigned int i = 1; i < xs.size(); i += 2) { - //pbpair.second = -1; + x_index = i - 1; v_index = i; // v' <- v + eta / 2 F(x) - Point z = F(v_index, xs_prev, t); - z = (eta / 2) * z; - xs[v_index] = xs[v_index] + z; + if (k == 0 && !accepted) { + grad_x = F(v_index, xs_prev, t); + } + xs[v_index] += (eta / 2) * grad_x; // x <- x + eta v' - Point y = xs[v_index]; + y = xs[v_index]; if (Ks[x_index] == NULL) { - xs[x_index] = xs_prev[x_index] + eta*y; + xs[x_index] += eta*y; } else { // Find intersection (assuming a line trajectory) between x and y @@ -173,10 +175,8 @@ struct LeapfrogODESolver { } // tilde v <- v + eta / 2 F(tilde x) - z = F(v_index, xs, t); - z = (eta / 2) * z; - xs[v_index] = xs[v_index] + z; - + grad_x = F(v_index, xs, t); + xs[v_index] += (eta / 2) * grad_x; } } @@ -202,6 +202,19 @@ struct LeapfrogODESolver { xs[index] = p; } + NT get_eta() { + return eta; + } + + void set_eta(NT &eta_) { + eta = eta_; + eta0 = eta_; + } + + bounds get_bounds() { + return Ks; + } + }; diff --git a/include/preprocess/estimate_L_smooth_parameter.hpp b/include/preprocess/estimate_L_smooth_parameter.hpp new file mode 100644 index 000000000..8f5a1e89c --- /dev/null +++ b/include/preprocess/estimate_L_smooth_parameter.hpp @@ -0,0 +1,73 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2012-2020 Vissarion Fisikopoulos +// Copyright (c) 2018-2020 Apostolos Chalkis + +//Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. + +// Licensed under GNU LGPL.3, see LICENCE file + + +#ifndef ESTIMATE_L_SMOOTH_PARAMETER_HPP +#define ESTIMATE_L_SMOOTH_PARAMETER_HPP + +#include "random_walks/random_walks.hpp" + +template +< + typename WalkTypePolicy = AcceleratedBilliardWalk, + typename Polytope, + typename Point, + typename NegativeGradientFunctor, + typename RandomNumberGenerator +> +double estimate_L_smooth(Polytope &P, Point &p, unsigned int const& walk_length, + NegativeGradientFunctor F, RandomNumberGenerator &rng) +{ + typedef typename Point::FT NT; + typedef typename WalkTypePolicy::template Walk + < + Polytope, + RandomNumberGenerator + > RandomWalk; + + P.ComputeInnerBall(); + + unsigned int d = P.dimension(); + unsigned int rnum = 5 * d; + std::vector randPoints(1); + std::vector vecPoint1; + std::vector vecPoint2; + std::vector< std::vector > listOfPoints; + Point F1; + + RandomWalk walk(P, p, rng); + for (unsigned int i=0; i::lowest(), Ltemp; + + for (auto pit=listOfPoints.begin(); pit!=(listOfPoints.end()-1); ++pit) + { + F1 = F(1, *pit, 0); + + for (auto qit=(pit+1); qit!=listOfPoints.end(); ++qit) + { + vecPoint2 = *qit; + Ltemp = (F1 - F(1, *qit, 0)).length() / ((*pit)[0] - (*qit)[0]).length(); + + if (Ltemp > L) + { + L = Ltemp; + } + } + } + return L; +} + + +#endif diff --git a/include/random_walks/hamiltonian_monte_carlo_walk.hpp b/include/random_walks/hamiltonian_monte_carlo_walk.hpp index 3c50ca5e9..23342ea57 100644 --- a/include/random_walks/hamiltonian_monte_carlo_walk.hpp +++ b/include/random_walks/hamiltonian_monte_carlo_walk.hpp @@ -114,12 +114,10 @@ struct HamiltonianMonteCarloWalk { }; - inline void apply( - RandomNumberGenerator &rng, - int walk_length=1, - bool metropolis_filter=true) + inline void apply(RandomNumberGenerator &rng, + int walk_length=1, + bool metropolis_filter=true) { - num_runs++; // Pick a random velocity @@ -156,6 +154,7 @@ struct HamiltonianMonteCarloWalk { } } else { x = x_tilde; + accepted = true; } discard_ratio = (1.0 * total_discarded_samples) / num_runs; diff --git a/include/random_walks/nuts_hmc_walk.hpp b/include/random_walks/nuts_hmc_walk.hpp new file mode 100644 index 000000000..857064c0e --- /dev/null +++ b/include/random_walks/nuts_hmc_walk.hpp @@ -0,0 +1,380 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2012-2022 Vissarion Fisikopoulos +// Copyright (c) 2018-2022 Apostolos Chalkis +// Copyright (c) 2020-2022 Elias Tsigaridas +// Copyright (c) 2020-2022 Marios Papachristou + +// Licensed under GNU LGPL.3, see LICENCE file + +// References +// Matthew D. Hoffman, Andrew Gelman. "The No-U-Turn Sampler: +// Adaptively Setting Path Lengths in Hamiltonian Monte Carlo", 2011. + +// Comment: Compared to [Matthew D. Hoffman, Andrew Gelman, 2011] +// we modify the step of Nesterov's algorithm in the burn in phase. + +#ifndef NUTS_HAMILTONIAN_MONTE_CARLO_WALK_HPP +#define NUTS_HAMILTONIAN_MONTE_CARLO_WALK_HPP + + +#include "generators/boost_random_number_generator.hpp" +#include "random_walks/gaussian_helpers.hpp" +#include "ode_solvers/ode_solvers.hpp" +#include "preprocess/estimate_L_smooth_parameter.hpp" + +struct NutsHamiltonianMonteCarloWalk { + + template + < + typename NT, + typename OracleFunctor + > + struct parameters { + NT epsilon; // tolerance in mixing + NT eta; // step size + + parameters( + OracleFunctor const& F, + unsigned int dim, + NT epsilon_=2) + { + epsilon = epsilon_; + eta = F.params.L > 0 ? 10.0 / (dim * sqrt(F.params.L)) : 0.005; + } + }; + + template + < + typename Point, + typename Polytope, + typename RandomNumberGenerator, + typename NegativeGradientFunctor, + typename NegativeLogprobFunctor, + typename Solver + > + struct Walk { + + typedef std::vector pts; + typedef typename Point::FT NT; + typedef std::vector bounds; + + // Hyperparameters of the sampler + parameters ¶ms; + + // Numerical ODE solver + Solver *solver; + + // Dimension + unsigned int dim; + + // Discarded Samples + long num_runs = 0; + long total_acceptance = 0; + + // Average acceptance probability + NT average_acceptance = 0; + + // References to xs + Point x, v; + + // Helper points + Point v_pl, v_min, v_min_j, v_pl_j, X_pl, X_pl_j, X_min, X, X_rnd_j, X_min_j, x_pl_min; + + // Gradient function + NegativeGradientFunctor &F; + + bool accepted; + + // Burnin parameters + NT eps_step, mu, log_tilde_eps, H_tilde, alpha, na; + const NT delta = NT(0.65), Delta_max = NT(1000), gamma = NT(0.05), t0 = NT(10), kk = NT(0.85); + + // Density exponent + NegativeLogprobFunctor &f; + + Walk(Polytope *P, + Point &p, + NegativeGradientFunctor &neg_grad_f, + NegativeLogprobFunctor &neg_logprob_f, + parameters ¶m, + bool burn_in_phase = true) : + params(param), + F(neg_grad_f), + f(neg_logprob_f) + { + dim = p.dimension(); + + v_pl.set_dimension(dim); + v_min.set_dimension(dim); + v_min_j.set_dimension(dim); + v_pl_j.set_dimension(dim); + X_pl.set_dimension(dim); + X_pl_j.set_dimension(dim); + X_min.set_dimension(dim); + X.set_dimension(dim); + X_rnd_j.set_dimension(dim); + X_min_j.set_dimension(dim); + x_pl_min.set_dimension(dim); + + eps_step = params.eta; + mu = std::log(10*eps_step); + log_tilde_eps = NT(0); + H_tilde = NT(0); + alpha = NT(0); + na = NT(0); + + // Starting point is provided from outside + x = p; + + accepted = false; + + // Initialize solver + solver = new Solver(0, params.eta, pts{x, x}, F, bounds{P, NULL}); + disable_adaptive(); + + if (burn_in_phase) + { + RandomNumberGenerator rng(dim); + burnin(rng); + } + }; + + + inline void burnin(RandomNumberGenerator &rng, + unsigned int N = 1000, + unsigned int walk_length=1) + { + reset_num_runs(); + Point p = x; + NT L; + + if ((solver->get_bounds())[0] == NULL) + { + L = (NT(100) / NT(dim)) * (NT(100) / NT(dim)); + } + else + { + Polytope K = *(solver->get_bounds())[0]; + L = estimate_L_smooth(K, p, walk_length, F, rng); + } + + eps_step = NT(5) / (NT(dim) * std::sqrt(L)); + solver->set_eta(eps_step); + + for (int i = 0; i < N; i++) + { + apply(rng, walk_length, true); + solver->set_eta(eps_step); + } + reset_num_runs(); + } + + + inline void apply(RandomNumberGenerator &rng, + unsigned int walk_length=1, + bool burnin = false) + { + num_runs++; + + int x_counting_total = 0; + + // Pick a random velocity + v = GetDirection::apply(dim, rng, false); + + v_pl = v; + v_min = NT(-1) * v; + X_pl = x; + X_min = x; + + NT h1 = hamiltonian(x, v); + + NT uu = std::log(rng.sample_urdist()) - h1; + int j = -1; + bool s = true; + bool updated = false; + bool pos_state_single = false; + + if (burnin) + { + alpha = NT(0); + } + + while (s) + { + j++; + + if (burnin) + { + na = std::pow(NT(2), NT(j)); + } + + NT dir = rng.sample_urdist(); + + if (dir > 0.5) + { + v = v_pl; + X = X_pl; + } + else + { + v = v_min; + X = X_min; + } + X_rnd_j = X; + + int x_counting = 0; + int num_samples = int(std::pow(NT(2), NT(j))); + accepted = false; + + for (int k = 1; k <= num_samples; k++) + { + if (!accepted) + { + solver->set_state(0, X); + solver->set_state(1, v); + } + + // Get proposals + solver->steps(walk_length, accepted); + accepted = true; + + X = solver->get_state(0); + v = solver->get_state(1); + + NT hj = hamiltonian(X, v); + + if (burnin) + { + alpha += std::min(NT(1), std::exp(-hj + h1)); + } + + if (uu > Delta_max - hj) + { + s = false; + break; + } + + bool pos_state = false; + if (uu < -hj) + { + pos_state = true; + pos_state_single = true; + x_counting = x_counting + 1; + x_counting_total = x_counting_total + 1; + } + + if (k == 1) + { + if (dir > 0.5) + { + X_min_j = X; + v_min_j = v; + } + else + { + X_pl_j = X; + v_pl_j = v; + } + } + if (k == num_samples) + { + if (dir > 0.5) + { + x_pl_min = X - X_min_j; + if ((x_pl_min.dot(v) < 0) || (x_pl_min.dot(v_min_j) < 0)) + { + s = false; + } + } + else + { + x_pl_min = X_pl_j - X; + if ((x_pl_min.dot(v) < 0) || (x_pl_min.dot(v_pl_j) < 0)) + { + s = false; + } + } + } + if ((rng.sample_urdist() < (1/NT(x_counting))) && pos_state) + { + X_rnd_j = X; + } + } + + if (dir > 0.5) + { + X_pl = X; + v_pl = v; + } + else + { + X_min = X; + v_min = v; + } + + if (s && (rng.sample_urdist() < (NT(x_counting) / NT(x_counting_total)))) + { + x = X_rnd_j; + if (pos_state_single) + { + updated = true; + } + } + + if (s) + { + x_pl_min = X_pl - X_min; + if ((x_pl_min.dot(v_min) < 0) || (x_pl_min.dot(v_pl) < 0)) + { + s = false; + } + } + } + + if (updated) + { + total_acceptance++; + } + average_acceptance = NT(total_acceptance) / NT(num_runs); + + if (burnin) + { + H_tilde = (NT(1) - NT(1) / (NT(num_runs) + t0)) * H_tilde + (NT(1) / (NT(num_runs) + t0)) * (delta - alpha / na); + NT log_eps = mu - (std::sqrt(NT(num_runs)) / gamma) * H_tilde; + + // TODO: use the following to generalize Nesterov's algorithm + //log_tilde_eps = std::pow(mu,-kk) * log_eps + (NT(1) - std::pow(mu,-kk))*log_tilde_eps; + + eps_step = std::exp(log_eps); + } + } + + inline NT hamiltonian(Point &pos, Point &vel) const { + return f(pos) + 0.5 * vel.dot(vel); + } + + inline NT get_eta_solver() { + return solver->get_eta(); + } + + void disable_adaptive() { + solver->disable_adaptive(); + } + + void enable_adaptive() { + solver->enable_adaptive(); + } + + void reset_num_runs() { + num_runs = 0; + total_acceptance = 0; + } + + NT get_ratio_acceptance() { + return average_acceptance; + } + }; +}; + +#endif // HAMILTONIAN_MONTE_CARLO_WALK_HPP diff --git a/include/random_walks/random_walks.hpp b/include/random_walks/random_walks.hpp index 4affee74d..6c36457c1 100644 --- a/include/random_walks/random_walks.hpp +++ b/include/random_walks/random_walks.hpp @@ -28,6 +28,7 @@ #include "random_walks/exponential_hamiltonian_monte_carlo_exact_walk.hpp" #include "random_walks/uniform_accelerated_billiard_walk_parallel.hpp" #include "random_walks/hamiltonian_monte_carlo_walk.hpp" +#include "random_walks/nuts_hmc_walk.hpp" #include "random_walks/langevin_walk.hpp" #include "random_walks/crhmc/crhmc_walk.hpp" #endif // RANDOM_WALKS_RANDOM_WALKS_HPP diff --git a/include/sampling/random_point_generators.hpp b/include/sampling/random_point_generators.hpp index 86b10666c..a2a811dd1 100644 --- a/include/sampling/random_point_generators.hpp +++ b/include/sampling/random_point_generators.hpp @@ -231,26 +231,15 @@ struct LogconcaveRandomPointGenerator { template - < - typename Polytope, - typename Point, - typename PointList, - typename WalkPolicy, - typename RandomNumberGenerator, - typename NegativeGradientFunctor, - typename NegativeLogprobFunctor, - typename Parameters + < typename PointList, + typename WalkPolicy, + typename RandomNumberGenerator > - static void apply(Polytope &P, - Point &p, // a point to start - unsigned int const& rnum, + static void apply(unsigned int const& rnum, unsigned int const& walk_length, PointList &randPoints, WalkPolicy &policy, RandomNumberGenerator &rng, - NegativeGradientFunctor &F, - NegativeLogprobFunctor &f, - Parameters ¶meters, Walk &walk) { typedef double NT; diff --git a/include/sampling/sampling.hpp b/include/sampling/sampling.hpp index 4457b9cb6..0fdab8702 100644 --- a/include/sampling/sampling.hpp +++ b/include/sampling/sampling.hpp @@ -23,10 +23,10 @@ #define SAMPLE_ONLY_H template void uniform_sampling(PointList &randPoints, Polytope &P, @@ -232,14 +232,14 @@ template < typename Solver > void logconcave_sampling(PointList &randPoints, - Polytope &P, - RandomNumberGenerator &rng, - const unsigned int &walk_len, - const unsigned int &rnum, - const Point &starting_point, - unsigned int const& nburns, - NegativeGradientFunctor &F, - NegativeLogprobFunctor &f) + Polytope &P, + RandomNumberGenerator &rng, + const unsigned int &walk_len, + const unsigned int &rnum, + const Point &starting_point, + unsigned int const& nburns, + NegativeGradientFunctor &F, + NegativeLogprobFunctor &f) { typedef typename WalkTypePolicy::template Walk < @@ -272,16 +272,16 @@ void logconcave_sampling(PointList &randPoints, walk logconcave_walk(&P, p, F, f, params); typedef LogconcaveRandomPointGenerator RandomPointGenerator; - - RandomPointGenerator::apply(P, p, nburns, walk_len, randPoints, - push_back_policy, rng, F, f, params, logconcave_walk); - + + if (nburns > 0) { + RandomPointGenerator::apply(nburns, walk_len, randPoints, + push_back_policy, rng, logconcave_walk); + } logconcave_walk.disable_adaptive(); randPoints.clear(); - RandomPointGenerator::apply(P, p, rnum, walk_len, randPoints, - push_back_policy, rng, F, f, params, logconcave_walk); - + RandomPointGenerator::apply(rnum, walk_len, randPoints, + push_back_policy, rng, logconcave_walk); } diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index ddd40e362..d95a49c7f 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -318,6 +318,11 @@ else () COMMAND logconcave_sampling_test -tc=uld) add_test(NAME logconcave_sampling_test_exponential_biomass_sampling COMMAND logconcave_sampling_test -tc=exponential_biomass_sampling) + add_test(NAME logconcave_sampling_test_nuts_hmc_truncated + COMMAND logconcave_sampling_test -tc=benchmark_nuts_hmc_truncated) + add_test(NAME logconcave_sampling_test_nuts_hmc + COMMAND logconcave_sampling_test -tc=benchmark_nuts_hmc) + add_executable (crhmc_sampling_test crhmc_sampling_test.cpp $) add_test(NAME crhmc_sampling_test_crhmc diff --git a/test/logconcave_sampling_test.cpp b/test/logconcave_sampling_test.cpp index f8f6901cd..8e53f489b 100644 --- a/test/logconcave_sampling_test.cpp +++ b/test/logconcave_sampling_test.cpp @@ -39,6 +39,8 @@ #include "generators/h_polytopes_generator.h" #include "generators/convex_bodies_generator.h" +#include "diagnostics/univariate_psrf.hpp" + #include "preprocess/svd_rounding.hpp" #include "misc/misc.h" @@ -308,6 +310,80 @@ void benchmark_hmc(bool truncated) { } +template +void benchmark_nuts_hmc(bool truncated) { + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef std::vector pts; + typedef HPolytope Hpolytope; + typedef BoostRandomNumberGenerator RandomNumberGenerator; + typedef CustomFunctor::GradientFunctor NegativeGradientFunctor; + typedef CustomFunctor::FunctionFunctor NegativeLogprobFunctor; + typedef LeapfrogODESolver Solver; + + typedef Eigen::Matrix MT; + typedef Eigen::Matrix VT; + + NegativeGradientFunctor F; + NegativeLogprobFunctor f; + RandomNumberGenerator rng(1); + unsigned int dim_min = 10; + unsigned int dim_max = 100; + int n_samples = 1000; + long ETA; + bool automatic_burnin = false; + std::chrono::time_point start, stop; + + for (unsigned int dim = dim_min; dim <= dim_max; dim+=10) + { + MT samples(dim, n_samples); + Point x0(dim); + NutsHamiltonianMonteCarloWalk::parameters hmc_params(F, dim); + if (truncated) + { + Hpolytope P = generate_cube(dim, false); + + std::cout << "eta0: " << hmc_params.eta << std::endl; + + NutsHamiltonianMonteCarloWalk::Walk + hmc(&P, x0, F, f, hmc_params, automatic_burnin); + + hmc.burnin(rng); + std::cout << "eta: " << hmc.get_eta_solver() << std::endl; + + start = std::chrono::high_resolution_clock::now(); + for (int i = 0; i < n_samples; i++) { + hmc.apply(rng); + samples.col(i) = hmc.x.getCoefficients(); + } + stop = std::chrono::high_resolution_clock::now(); + std::cout << "proportion of sucussfull steps: " << hmc.get_ratio_acceptance() << std::endl; + } + else + { + NutsHamiltonianMonteCarloWalk::Walk + hmc(NULL, x0, F, f, hmc_params, automatic_burnin); + + hmc.burnin(rng); + std::cout << "eta: " << hmc.get_eta_solver() << std::endl; + + start = std::chrono::high_resolution_clock::now(); + for (int i = 0; i < n_samples; i++) { + hmc.apply(rng); + samples.col(i) = hmc.x.getCoefficients(); + } + stop = std::chrono::high_resolution_clock::now(); + std::cout << "proportion of sucussfull steps: " << hmc.get_ratio_acceptance() << std::endl; + } + + std::cout << "PSRF: " << univariate_psrf(samples).maxCoeff() << std::endl; + + ETA = (long) std::chrono::duration_cast(stop - start).count(); + std::cout<< "time: " << ETA << "\n" << std::endl; + } + +} + template void test_hmc() { typedef Cartesian Kernel; @@ -878,6 +954,11 @@ void call_test_benchmark_hmc(bool truncated) { benchmark_hmc(truncated); } +template +void call_test_benchmark_nuts_hmc(bool truncated) { + benchmark_nuts_hmc(truncated); +} + template void call_test_benchmark_polytopes_grid_search() { typedef Cartesian Kernel; @@ -1196,6 +1277,14 @@ TEST_CASE("exponential_biomass_sampling") { call_test_exp_sampling(); } +TEST_CASE("benchmark_nuts_hmc_truncated") { + call_test_benchmark_nuts_hmc(true); +} + +TEST_CASE("benchmark_nuts_hmc") { + call_test_benchmark_nuts_hmc(false); +} + TEST_CASE("benchmark_hmc") { call_test_benchmark_hmc(false); } From c31e98bdc9b3fa1fe1b1b13e9a95a899d4f33eb9 Mon Sep 17 00:00:00 2001 From: iakoviid <44919975+iakoviid@users.noreply.github.com> Date: Thu, 2 Nov 2023 03:24:04 -0600 Subject: [PATCH 03/35] Use of SIMD computation in CRHMC walk [fixed] (#286) --- R-proj/examples/logconcave/simple_crhmc.R | 49 + R-proj/examples/logconcave/sparse_crhmc.R | 98 + R-proj/man/Rcpp_sparse_constraint_problem.Rd | 34 + R-proj/man/sample_points.Rd | 4 +- R-proj/man/sparse_constraint_problem.Rd | 26 + R-proj/src/Makevars | 9 +- R-proj/src/Makevars.win | 9 +- R-proj/src/oracle_functors_rcpp.h | 39 +- R-proj/src/polytopes_modules.cpp | 67 +- R-proj/src/sample_points.cpp | 107 +- R-proj/tests/testthat/test_sampling.R | 35 +- cran_gen/Makevars | 9 +- cran_gen/Makevars.win | 9 +- cran_gen/genCRANpkg.R | 16 +- examples/crhmc_prepare/CMakeLists.txt | 4 +- examples/crhmc_sampling/.gitignore | 4 + examples/crhmc_sampling/CMakeLists.txt | 9 +- examples/crhmc_sampling/README.md | 11 +- examples/crhmc_sampling/crhmc_sampling.cpp | 153 - examples/crhmc_sampling/data/afiro.mm | 112 + examples/crhmc_sampling/data/afiro_bounds.mm | 54 + 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+++++ external/PackedCSparse/{ => qd}/dd_real.h | 44 +- external/PackedCSparse/qd/fpu.cc | 124 + external/PackedCSparse/qd/fpu.h | 39 + external/PackedCSparse/qd/inline.h | 143 + external/PackedCSparse/qd/qd.pdf | Bin 0 -> 194502 bytes external/PackedCSparse/qd/qd_config.h | 92 + external/PackedCSparse/qd/qd_const.cc | 62 + external/PackedCSparse/qd/qd_inline.h | 1047 ++++ external/PackedCSparse/qd/qd_real.cc | 2624 ++++++++++ external/PackedCSparse/qd/qd_real.h | 296 ++ external/PackedCSparse/qd/util.cc | 22 + external/PackedCSparse/qd/util.h | 4 + external/cmake-files/QD.cmake | 64 +- include/ode_solvers/implicit_midpoint.hpp | 47 +- include/ode_solvers/oracle_functors.hpp | 10 +- include/ode_solvers/oracle_functors_rcpp.hpp | 36 + include/preprocess/crhmc/analytic_center.h | 21 +- include/preprocess/crhmc/constraint_problem.h | 81 + include/preprocess/crhmc/crhmc_input.h | 75 +- include/preprocess/crhmc/crhmc_problem.h | 367 +- include/preprocess/crhmc/crhmc_utils.h | 135 +- include/preprocess/crhmc/lewis_center.h | 21 +- include/preprocess/crhmc/opts.h | 15 +- include/preprocess/crhmc/two_sided_barrier.h | 57 +- .../crhmc/weighted_two_sided_barrier.h | 40 +- .../crhmc/additional_units/auto_tuner.hpp | 15 +- .../additional_units/dynamic_regularizer.hpp | 23 +- .../additional_units/dynamic_step_size.hpp | 57 +- .../crhmc/additional_units/dynamic_weight.hpp | 32 +- include/random_walks/crhmc/crhmc_walk.hpp | 131 +- include/random_walks/crhmc/hamiltonian.hpp | 183 +- include/sampling/random_point_generators.hpp | 59 + include/sampling/sampling.hpp | 153 +- test/CMakeLists.txt | 16 +- test/benchmarks_crhmc.cpp | 3 +- test/benchmarks_crhmc_sampling.cpp | 130 +- test/crhmc_polytope_preparation_test.cpp | 10 + test/crhmc_sampling_test.cpp | 133 +- test/netlib/degen2.mm | 4447 +++++++++++++++++ test/netlib/degen2_bounds.mm | 760 +++ test/ode_solvers_test.cpp | 17 +- 84 files changed, 15594 insertions(+), 989 deletions(-) create mode 100644 R-proj/examples/logconcave/simple_crhmc.R create mode 100644 R-proj/examples/logconcave/sparse_crhmc.R create mode 100644 R-proj/man/Rcpp_sparse_constraint_problem.Rd create mode 100644 R-proj/man/sparse_constraint_problem.Rd delete mode 100644 examples/crhmc_sampling/crhmc_sampling.cpp create mode 100644 examples/crhmc_sampling/data/afiro.mm create mode 100644 examples/crhmc_sampling/data/afiro_bounds.mm create mode 100644 examples/crhmc_sampling/data/cube2d.jpg create mode 100644 examples/crhmc_sampling/data/simplex2d.jpg create mode 100644 examples/crhmc_sampling/data/simplex3.mm create mode 100644 examples/crhmc_sampling/data/simplex3_bounds.mm create mode 100644 examples/crhmc_sampling/helper_functions.hpp create mode 100644 examples/crhmc_sampling/sampling_functions.cpp create mode 100644 examples/crhmc_sampling/simple_crhmc.cpp delete mode 100644 examples/optimization_spectrahedra/readWriteSdpaFile.cpp create mode 100644 external/PackedCSparse/qd/COPYING create mode 100644 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create mode 100644 external/PackedCSparse/qd/qd_inline.h create mode 100644 external/PackedCSparse/qd/qd_real.cc create mode 100644 external/PackedCSparse/qd/qd_real.h create mode 100644 external/PackedCSparse/qd/util.cc create mode 100644 external/PackedCSparse/qd/util.h create mode 100644 include/preprocess/crhmc/constraint_problem.h create mode 100644 test/netlib/degen2.mm create mode 100644 test/netlib/degen2_bounds.mm diff --git a/R-proj/examples/logconcave/simple_crhmc.R b/R-proj/examples/logconcave/simple_crhmc.R new file mode 100644 index 000000000..5fee2b8b8 --- /dev/null +++ b/R-proj/examples/logconcave/simple_crhmc.R @@ -0,0 +1,49 @@ +# VolEsti (volume computation and sampling library) + +# Copyright (c) 2012-2020 Vissarion Fisikopoulos +# Copyright (c) 2018-2020 Apostolos Chalkis +# Copyright (c) 2020-2020 Marios Papachristou +# Copyright (c) 2022-2022 Ioannis Iakovidis + +# Contributed and/or modified by Ioannis Iakovidis, as part of Google Summer of Code 2022 program. + +# Licensed under GNU LGPL.3, see LICENCE file + +# Example script for using the logconcave sampling methods + +# Import required libraries +library(volesti) + +# Sampling from uniform density example + +logconcave_sample<- function(P,distribution, n_samples ,n_burns){ + if (distribution == "uniform"){ + f <- function(x) (0) + grad_f <- function(x) (0) + L=1 + m=1 + pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f, "L_" = L, "m" = m)) + return(max(psrf_univariate(pts, "interval"))) + } + else if(distribution == "gaussian"){ + pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "variance"=8)) + return(max(psrf_univariate(pts, "interval"))) + } +} + +for (i in 1:2) { + + if (i==1) { + distribution = 'gaussian' + cat("Gaussian ") + } else { + distribution = 'uniform' + cat("Uniform ") + } + + P = gen_simplex(10, 'H') + psrf = logconcave_sample(P,distribution,5000,2000) + cat("psrf = ") + cat(psrf) + cat("\n") +} diff --git a/R-proj/examples/logconcave/sparse_crhmc.R b/R-proj/examples/logconcave/sparse_crhmc.R new file mode 100644 index 000000000..373371986 --- /dev/null +++ b/R-proj/examples/logconcave/sparse_crhmc.R @@ -0,0 +1,98 @@ +# VolEsti (volume computation and sampling library) + +# Copyright (c) 2012-2020 Vissarion Fisikopoulos +# Copyright (c) 2018-2020 Apostolos Chalkis +# Copyright (c) 2020-2020 Marios Papachristou +# Copyright (c) 2022-2022 Ioannis Iakovidis + +# Contributed and/or modified by Ioannis Iakovidis, as part of Google Summer of Code 2022 program. + +# Licensed under GNU LGPL.3, see LICENCE file + +# Example script for using the logconcave sampling methods + +# Import required libraries +library(ggplot2) +library(volesti) + +# Sampling from logconcave density example + +# Helper function +norm_vec <- function(x) sqrt(sum(x^2)) + +# Negative log-probability oracle +f <- function(x) (norm_vec(x)^2 + sum(x)) + +# Negative log-probability gradient oracle +grad_f <- function(x) (2 * x + 1) + +# Interval [-1, 1] +A = matrix(c(1, -1), ncol=1, nrow=2, byrow=TRUE) +b = c(2,1) + +# Create domain of truncation +P <- volesti::Hpolytope$new(A, b) + +# Mode of logconcave density +x_min <- c(-0.5) + +# Smoothness and strong-convexity +L <- 2 +m <- 2 + +# Sample points +n_samples <- 80000 +n_burns <- n_samples / 2 +cat("---Sampling without hessian\n") +pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "step_size" = 0.3, "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f, "L_" = L, "m" = m)) +jpeg("histogram_without_hessian.jpg") +# Plot histogram +hist(pts, probability=TRUE, breaks = 100) + +cat("Sample mean is: ") +sample_mean <- mean(pts) +cat(sample_mean) +cat("\n") +cat("Sample variance is: ") +sample_variance <- mean((pts - sample_mean)^2) +cat(sample_variance) +cat("\n") +invisible(capture.output(dev.off())) + +# Negative log-probability hessian oracle +hess_f <- function(x) (2) +cat("---Sampling with hessian\n") +pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "step_size" = 0.3, "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f,"negative_logprob_hessian" = hess_f, "L_" = L, "m" = m)) +jpeg("histogram_with_hessian.jpg") +# Plot histogram +hist(pts, probability=TRUE, breaks = 100) + +cat("Sample mean is: ") +sample_mean <- mean(pts) +cat(sample_mean) +cat("\n") +cat("Sample variance is: ") +sample_variance <- mean((pts - sample_mean)^2) +cat(sample_variance) +cat("\n") +invisible(capture.output(dev.off())) + +walk="CRHMC" +library(Matrix) +bineq=matrix(c(10,10,10,10,10), nrow=5, ncol=1, byrow=TRUE) +Aineq = matrix(c(1,0,-0.25,-1,2.5,1,0.4,-1,-0.9,0.5), nrow=5, ncol=2, byrow = TRUE) +Aineq = as( Aineq, 'dgCMatrix' ) +beq=matrix(,nrow=0, ncol=1, byrow=TRUE) +Aeq = matrix(, nrow=0, ncol=2, byrow = TRUE) +Aeq=as( Aeq, 'dgCMatrix' ) +lb=-100000*c(1,1); +ub=100000*c(1,1); +cat("---Sampling the normal distribution in a pentagon\n") +P <- volesti::sparse_constraint_problem$new(Aineq, bineq,Aeq, beq, lb, ub) +points <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "step_size" = 0.3, "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "variance" = 8)) +jpeg("pentagon.jpg") +plot(ggplot(data.frame( x=points[1,], y=points[2,] )) + +geom_point( aes(x=x, y=y, color=walk)) + coord_fixed(xlim = c(-15,15), +ylim = c(-15,15)) + ggtitle(sprintf("Sampling a random pentagon with walk %s", walk))) +invisible(capture.output(dev.off())) +write.table(points, file="pentagon.txt", row.names=FALSE, col.names=FALSE) diff --git a/R-proj/man/Rcpp_sparse_constraint_problem.Rd b/R-proj/man/Rcpp_sparse_constraint_problem.Rd new file mode 100644 index 000000000..82f81f6c9 --- /dev/null +++ b/R-proj/man/Rcpp_sparse_constraint_problem.Rd @@ -0,0 +1,34 @@ +\docType{class} +\name{Rcpp_sparse_constraint_problem} +\alias{Rcpp_sparse_constraint_problem-class} +\alias{[,Rcpp_sparse_constraint_problem-method} +\alias{[,Rcpp_sparse_constraint_problem,ANY,ANY,ANY-method} +\alias{$<-,Rcpp_sparse_constraint_problem-method} +\alias{$,Rcpp_sparse_constraint_problem-method} +\alias{filepaths<-,Rcpp_sparse_constraint_problem-method} +\title{ +An \code{Rcpp} class to represent sparse_constraint_problems, exposed to \code{R} via modules. +} +\description{ +A constraint problem is defined by a set of linear inequalities and equalities or equivalently a \eqn{d}-dimensional constraint problem is defined by a \eqn{mineq\times d} matrix Aineq and a \eqn{mineq}-dimensional vector bineq, s.t.: \eqn{Aineqx\leq bineq}, a \eqn{meq\times d} matrix Aeq and a \eqn{meq}-dimensional vector beq, s.t.: \eqn{Aeqx= beq} and two \eqn{d} vectors lb, ub such that \eqn{lb\leq x \leq ub}.} +\details{ +\describe{ +\item{\code{Aineq} }{\eqn{mineq\times d} sparse matrix Aineq} + +\item{\code{bineq} }{\eqn{mineq}-dimensional vector bineq} + +\item{\code{Aeq} }{\eqn{meq\times d} sparse matrix Aeq} + +\item{\code{beq} }{\eqn{meq}-dimensional vector beq} + +\item{\code{lb} }{\eqn{d}-dimensional vector bineq} + +\item{\code{ub} }{\eqn{d}-dimensional vector bineq} + +\item{\code{type} }{An integer that declares the representation of the polytope. For sparse_constraint_problem the default value is 5.} + +\item{\code{dimension} }{The dimension of the polytope.} + + } +} +\keyword{internal} diff --git a/R-proj/man/sample_points.Rd b/R-proj/man/sample_points.Rd index ca2ef87fa..e969e557d 100644 --- a/R-proj/man/sample_points.Rd +++ b/R-proj/man/sample_points.Rd @@ -7,13 +7,13 @@ sample_points(P, n, random_walk = NULL, distribution = NULL, seed = NULL) } \arguments{ -\item{P}{A convex polytope. It is an object from class (a) Hpolytope or (b) Vpolytope or (c) Zonotope or (d) VpolytopeIntersection.} +\item{P}{A convex polytope. It is an object from class (a) Hpolytope or (b) Vpolytope or (c) Zonotope or (d) VpolytopeIntersection or (e) sparse_constraint_problem.} \item{n}{The number of points that the function is going to sample from the convex polytope.} \item{random_walk}{Optional. A list that declares the random walk and some related parameters as follows: \itemize{ -\item{\code{walk} }{ A string to declare the random walk: i) \code{'CDHR'} for Coordinate Directions Hit-and-Run, ii) \code{'RDHR'} for Random Directions Hit-and-Run, iii) \code{'BaW'} for Ball Walk, iv) \code{'BiW'} for Billiard walk, v) \code{'dikin'} for dikin walk, vi) \code{'vaidya'} for vaidya walk, vii) \code{'john'} for john walk, viii) \code{'BCDHR'} boundary sampling by keeping the extreme points of CDHR or ix) \code{'BRDHR'} boundary sampling by keeping the extreme points of RDHR x) \code{'NUTS'} for NUTS Hamiltonian Monte Carlo sampler (logconcave densities) xi) \code{'HMC'} for Hamiltonian Monte Carlo (logconcave densities) xii) \code{'ULD'} for Underdamped Langevin Dynamics using the Randomized Midpoint Method (logconcave densities) xiii) \code{'ExactHMC'} for exact Hamiltonian Monte Carlo with reflections (spherical Gaussian or exponential distribution). The default walk is \code{'aBiW'} for the uniform distribution, \code{'CDHR'} for the Gaussian distribution and H-polytopes and \code{'BiW'} or \code{'RDHR'} for the same distributions and V-polytopes and zonotopes. \code{'NUTS'} is the default sampler for logconcave densities.} +\item{\code{walk} }{ A string to declare the random walk: i) \code{'CDHR'} for Coordinate Directions Hit-and-Run, ii) \code{'RDHR'} for Random Directions Hit-and-Run, iii) \code{'BaW'} for Ball Walk, iv) \code{'BiW'} for Billiard walk, v) \code{'dikin'} for dikin walk, vi) \code{'vaidya'} for vaidya walk, vii) \code{'john'} for john walk, viii) \code{'BCDHR'} boundary sampling by keeping the extreme points of CDHR or ix) \code{'BRDHR'} boundary sampling by keeping the extreme points of RDHR, x) \code{'NUTS'} for NUTS Hamiltonian Monte Carlo sampler (logconcave densities), xi) \code{'HMC'} for Hamiltonian Monte Carlo (logconcave densities), xii) CRHMC for Riemannian HMC with H-polytope constraints (uniform and general logconcave densities), xiii) \code{'ULD'} for Underdamped Langevin Dynamics using the Randomized Midpoint Method (logconcave densities), xiii) \code{'ExactHMC'} for exact Hamiltonian Monte Carlo with reflections (spherical Gaussian or exponential distribution). The default walk is \code{'aBiW'} for the uniform distribution, \code{'CDHR'} for the Gaussian distribution and H-polytopes and \code{'BiW'} or \code{'RDHR'} for the same distributions and V-polytopes and zonotopes. \code{'NUTS'} is the default sampler for logconcave densities and \code{'CRHMC'} for logconcave densities with H-polytope and sparse constrainted problems.} \item{\code{walk_length} }{ The number of the steps per generated point for the random walk. The default value is \eqn{1}.} \item{\code{nburns} }{ The number of points to burn before start sampling. The default value is \eqn{1}.} \item{\code{starting_point} }{ A \eqn{d}-dimensional numerical vector that declares a starting point in the interior of the polytope for the random walk. The default choice is the center of the ball as that one computed by the function \code{inner_ball()}.} diff --git a/R-proj/man/sparse_constraint_problem.Rd b/R-proj/man/sparse_constraint_problem.Rd new file mode 100644 index 000000000..535dc8426 --- /dev/null +++ b/R-proj/man/sparse_constraint_problem.Rd @@ -0,0 +1,26 @@ +\name{sparse_constraint_problem} +\alias{sparse_constraint_problem} +\title{An \code{R} class to represent sparse constraint problems.} + +\description{ +A constraint problem is defined by a set of linear inequalities and equalities or equivalently a \eqn{d}-dimensional constraint problem is defined by a \eqn{mineq\times d} matrix Aineq and a \eqn{mineq}-dimensional vector bineq, s.t.: \eqn{Aineqx\leq bineq}, a \eqn{meq\times d} matrix Aeq and a \eqn{meq}-dimensional vector beq, s.t.: \eqn{Aeqx\eq beq} and two \eqn{d} vectors lb, ub such that \eqn{lb\leq x \leq ub}. +} +\section{Fields}{ +\itemize{ +\item{\code{Aineq} }{\eqn{mineq\times d} sparse matrix Aineq} + +\item{\code{bineq} }{\eqn{mineq}-dimensional vector bineq} + +\item{\code{Aeq} }{\eqn{meq\times d} sparse matrix Aeq} + +\item{\code{beq} }{\eqn{meq}-dimensional vector beq} + +\item{\code{lb} }{\eqn{d}-dimensional vector bineq} + +\item{\code{ub} }{\eqn{d}-dimensional vector bineq} + +\item{\code{type} }{An integer that declares the representation of the polytope. For sparse_constraint_problem the default value is 5.} + +\item{\code{dimension} }{The dimension of the polytope.} + +}} diff --git a/R-proj/src/Makevars b/R-proj/src/Makevars index 8f401249e..b642e2fbe 100644 --- a/R-proj/src/Makevars +++ b/R-proj/src/Makevars @@ -4,11 +4,16 @@ PKG_CXXFLAGS= -Wno-deprecated-declarations -lm -ldl -Wno-ignored-attributes -DBO CXX_STD = CXX11 -PKG_LIBS=-LRproj_externals/lp_solve -llp_solve $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) +PKG_LIBS=-LRproj_externals/lp_solve -llp_solve -L../../external/PackedCSparse/qd -lqd $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) -$(SHLIB): Rproj_externals/lp_solve/liblp_solve.a +$(SHLIB): Rproj_externals/lp_solve/liblp_solve.a ../../external/PackedCSparse/qd/libqd.a Rproj_externals/lp_solve/liblp_solve.a: @(cd Rproj_externals/lp_solve && $(MAKE) liblp_solve.a \ CC="$(CC)" CPPFLAGS="$(CPPFLAGS)" CFLAGS="$(CFLAGS)" \ CPICFLAGS="$(CPICFLAGS)" AR="$(AR)" RANLIB="$(RANLIB)") + +../../external/PackedCSparse/qd/libqd.a: + @(cd ../../PackedCSparse/qd && $(MAKE) libqd.a \ + CC="$(CC)" CPPFLAGS="$(CPPFLAGS)" CFLAGS="$(CFLAGS)" \ + CPICFLAGS="$(CPICFLAGS)" AR="$(AR)") diff --git a/R-proj/src/Makevars.win b/R-proj/src/Makevars.win index 867308b75..17d28e206 100644 --- a/R-proj/src/Makevars.win +++ b/R-proj/src/Makevars.win @@ -2,12 +2,17 @@ PKG_CPPFLAGS=-I../../external/boost -I../../external/LPsolve_src/run_headers -I. PKG_CXXFLAGS= -Wno-deprecated-declarations -lm -ldl -Wno-ignored-attributes -DBOOST_NO_AUTO_PTR -DDISABLE_NLP_ORACLES CXX_STD = CXX11 -PKG_LIBS=-LRproj_externals/lp_solve -llp_solve $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) +PKG_LIBS=-LRproj_externals/lp_solve -llp_solve -L../../external/PackedCSparse/qd -lqd $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) -$(SHLIB): Rproj_externals/lp_solve/liblp_solve.a +$(SHLIB): Rproj_externals/lp_solve/liblp_solve.a ../../external/PackedCSparse/qd/libqd.a Rproj_externals/lp_solve/liblp_solve.a: @(cd Rproj_externals/lp_solve && $(MAKE) liblp_solve.a \ CC="$(CC)" CPPFLAGS="$(CPPFLAGS) -DUSRDLL -DINLINE=static" \ CFLAGS="$(CFLAGS)" CPICFLAGS="$(CPICFLAGS)" AR="$(AR)" \ RANLIB="$(RANLIB)") + +../../external/PackedCSparse/qd/libqd.a: + @(cd ../../PackedCSparse/qd && $(MAKE) libqd.a \ + CC="$(CC)" CPPFLAGS="$(CPPFLAGS)" CFLAGS="$(CFLAGS)" \ + CPICFLAGS="$(CPICFLAGS)" AR="$(AR)") diff --git a/R-proj/src/oracle_functors_rcpp.h b/R-proj/src/oracle_functors_rcpp.h index 558ac1b07..6929540ca 100644 --- a/R-proj/src/oracle_functors_rcpp.h +++ b/R-proj/src/oracle_functors_rcpp.h @@ -17,7 +17,8 @@ enum ode_solvers { euler, runge_kutta, richardson, - collocation + collocation, + implicit_midpoint }; // Holds Oracle Functor that wraps an R function via RCpp @@ -91,6 +92,17 @@ struct RcppFunctor { } } + Point operator() (Point const& x) const { + VT y = Rcpp::as(neg_grad_f(Rcpp::wrap(x.getCoefficients()))); + + Point z(y); + + if (negate) z = (-1.0) * z; + + // Return result as Point + return z; + } + }; // Negative log-probability functor @@ -118,6 +130,31 @@ struct RcppFunctor { }; + // Log-probability hessian functor + template + < + typename Point + > + struct HessianFunctor { + typedef typename Point::FT NT; + typedef typename Point::Coeff VT; + + parameters params; + Rcpp::Function hessian; // Negative hessian as an Rcpp::Function + + HessianFunctor( + parameters params_, + Rcpp::Function hessian_) : + params(params_), + hessian(hessian_) + {}; + + Point operator() (Point const& x) const { + VT y= Rcpp::as(hessian(Rcpp::wrap(x.getCoefficients()))); + return Point(y); + } + + }; }; #endif diff --git a/R-proj/src/polytopes_modules.cpp b/R-proj/src/polytopes_modules.cpp index 88c036988..443251fbb 100644 --- a/R-proj/src/polytopes_modules.cpp +++ b/R-proj/src/polytopes_modules.cpp @@ -11,11 +11,11 @@ class Hpolytope { Hpolytope() {} Hpolytope(Rcpp::NumericMatrix _A, Rcpp::NumericVector _b) : A(_A), b(_b), Aeq(Rcpp::NumericMatrix(0,0)), beq(Rcpp::NumericVector(0)), vol(std::numeric_limits::signaling_NaN()), dimension(_A.ncol()), type(1) {} - Hpolytope(Rcpp::NumericMatrix _A, Rcpp::NumericVector _b, Rcpp::NumericMatrix _Aeq, Rcpp::NumericVector _beq) : + Hpolytope(Rcpp::NumericMatrix _A, Rcpp::NumericVector _b, Rcpp::NumericMatrix _Aeq, Rcpp::NumericVector _beq) : A(_A), b(_b), Aeq(_Aeq), beq(_beq), vol(std::numeric_limits::signaling_NaN()), dimension(_A.ncol()), type(1) {} Hpolytope(Rcpp::NumericMatrix _A, Rcpp::NumericVector _b, double volume) : A(_A), b(_b), Aeq(Rcpp::NumericMatrix(0,0)), beq(Rcpp::NumericVector(0)), vol(volume), dimension(_A.ncol()), type(1) {} - Hpolytope(Rcpp::NumericMatrix _A, Rcpp::NumericVector _b, Rcpp::NumericMatrix _Aeq, Rcpp::NumericVector _beq, + Hpolytope(Rcpp::NumericMatrix _A, Rcpp::NumericVector _b, Rcpp::NumericMatrix _Aeq, Rcpp::NumericVector _beq, double volume) : A(_A), b(_b), Aeq(_Aeq), beq(_beq), vol(volume), dimension(_A.ncol()), type(1) {} Rcpp::NumericMatrix A; Rcpp::NumericVector b; @@ -59,7 +59,27 @@ class VPinterVP { unsigned int dimension; int type; }; - +#include +using SpMat=Eigen::SparseMatrix ; +class sparse_constraint_problem { +public: + sparse_constraint_problem() {} + sparse_constraint_problem(SpMat _Aineq, Rcpp::NumericVector _bineq, SpMat _Aeq, Rcpp::NumericVector _beq) : Aineq(_Aineq), bineq(_bineq), Aeq(_Aeq), beq(_beq), + lb(Rcpp::NumericVector(_Aineq.cols(),-1e9)), ub(Rcpp::NumericMatrix(_Aineq.cols(),1e9)), dimension(_Aineq.cols()), type(5) {} + sparse_constraint_problem(SpMat _Aineq, Rcpp::NumericVector _bineq, SpMat _Aeq, Rcpp::NumericVector _beq, + Rcpp::NumericVector _lb, Rcpp::NumericVector _ub) : Aineq(_Aineq), bineq(_bineq), Aeq(_Aeq), beq(_beq), + lb(_lb), ub(_ub), dimension(_Aineq.cols()), type(5) {} + sparse_constraint_problem(SpMat _Aeq, Rcpp::NumericVector _beq) : Aineq(SpMat(0, _Aeq.cols())), bineq(Rcpp::NumericVector(0)), Aeq(_Aeq), beq(_beq), + lb(Rcpp::NumericVector(_Aeq.cols(),-1e9)), ub(Rcpp::NumericMatrix(_Aeq.cols(),1e9)), dimension(_Aeq.cols()), type(5) {} + SpMat Aineq; + Rcpp::NumericVector bineq; + SpMat Aeq; + Rcpp::NumericVector beq; + Rcpp::NumericVector lb; + Rcpp::NumericVector ub; + unsigned int dimension; + int type; +}; RCPP_MODULE(polytopes){ using namespace Rcpp ; @@ -174,6 +194,47 @@ RCPP_MODULE(polytopes){ .field( "volume", &VPinterVP::vol ) .field( "dimension", &VPinterVP::dimension ) .field( "type", &VPinterVP::type ); + + //' An exposed class to represent a sparse constraint problem + //' + //' @description A constraint problem is defined by a set of linear inequalities and equalities or equivalently a \eqn{d}-dimensional constraint problem is defined by a \eqn{mineq\times d} matrix Aineq and a \eqn{mineq}-dimensional vector bineq, s.t.: \eqn{Aineqx\leq bineq}, a \eqn{meq\times d} matrix Aeq and a \eqn{meq}-dimensional vector beq, s.t.: \eqn{Aeqx\eq beq} and two \eqn{d} vectors lb, ub such that \eqn{lb\leq x \leq ub}. + //' + //' @field Aineq \eqn{mineq\times d} sparse matrix Aineq + //' @field bineq \eqn{mineq}-dimensional vector bineq + //' @field Aeq \eqn{meq\times d} sparse matrix Aeq + //' @field beq \eqn{meq}-dimensional vector beq + //' @field lb \eqn{d}-dimensional vector lb + //' @field ub \eqn{d}-dimensional vector ub + //' @field dimension An integer that declares the dimension of the polytope. It has not be given to the constructor. + //' @field type An integer that declares the representation of the polytope. For sparse_constraint_problem the default value is 5. It has not be given to the constructor. + //' + //' @example + //' # create a 2-d unit simplex + //' Aineq = matrix(, nrow=0, ncol=2, byrow = TRUE) + //' Aineq = as( Aineq, 'dgCMatrix' ) + //' bineq= matrix(,nrow=0, ncol=1, byrow=TRUE) + //' Aeq = matrix(c(1,1), ncol=2, nrow=1, byrow=TRUE) + //' Aeq = as( Aeq, 'dgCMatrix' ) + //' beq = c(1) + //' lb = c(0,0) + //' ub = c(1,1) + //' P = sparse_constraint_problem$new(Aineq, bineq, Aeq, beq, lb, ub) + //' @export + class_("sparse_constraint_problem") + // expose the default constructor + .constructor() + .constructor() + .constructor() + .constructor() + + .field( "Aineq", &sparse_constraint_problem::Aineq ) + .field( "bineq", &sparse_constraint_problem::bineq ) + .field( "Aeq", &sparse_constraint_problem::Aeq ) + .field( "beq", &sparse_constraint_problem::beq ) + .field( "lb", &sparse_constraint_problem::lb ) + .field( "ub", &sparse_constraint_problem::ub ) + .field( "dimension", &sparse_constraint_problem::dimension ) + .field( "type", &sparse_constraint_problem::type ); } extern SEXP _rcpp_module_boot_polytopes(void); diff --git a/R-proj/src/sample_points.cpp b/R-proj/src/sample_points.cpp index 4756d0915..1014e74ea 100644 --- a/R-proj/src/sample_points.cpp +++ b/R-proj/src/sample_points.cpp @@ -23,7 +23,7 @@ #include "sampling/sampling.hpp" #include "ode_solvers/ode_solvers.hpp" #include "oracle_functors_rcpp.h" - +#include "preprocess/crhmc/constraint_problem.h" enum random_walks { ball_walk, rdhr, @@ -39,7 +39,8 @@ enum random_walks { nuts, gaussian_hmc, exponential_hmc, - uld + uld, + crhmc }; template < @@ -49,7 +50,8 @@ template < typename NT, typename Point, typename NegativeGradientFunctor, - typename NegativeLogprobFunctor + typename NegativeLogprobFunctor, + typename HessianFunctor > void sample_from_polytope(Polytope &P, int type, RNGType &rng, PointList &randPoints, unsigned int const& walkL, unsigned int const& numpoints, @@ -57,7 +59,7 @@ void sample_from_polytope(Polytope &P, int type, RNGType &rng, PointList &randPo Point const& StartingPoint, unsigned int const& nburns, bool const& set_L, NT const& eta, random_walks walk, NegativeGradientFunctor *F=NULL, NegativeLogprobFunctor *f=NULL, - ode_solvers solver_type = no_solver) + HessianFunctor *h=NULL, ode_solvers solver_type = no_solver) { switch (walk) { @@ -214,6 +216,10 @@ void sample_from_polytope(Polytope &P, int type, RNGType &rng, PointList &randPo } break; + case crhmc:{ + execute_crhmc(P, rng, randPoints, walkL, numpoints, nburns, F, f, h); + break; + } case nuts: logconcave_sampling < @@ -266,15 +272,16 @@ void sample_from_polytope(Polytope &P, int type, RNGType &rng, PointList &randPo //' @param random_walk Optional. A list that declares the random walk and some related parameters as follows: //' \itemize{ //' \item{\code{walk} }{ A string to declare the random walk: i) \code{'CDHR'} for Coordinate Directions Hit-and-Run, -//' ii) \code{'RDHR'} for Random Directions Hit-and-Run, iii) \code{'BaW'} for Ball Walk, -//' iv) \code{'BiW'} for Billiard walk, v) \code{'dikin'} for dikin walk, vi) \code{'vaidya'} for vaidya walk, -//' vii) \code{'john'} for john walk, viii) \code{'BCDHR'} boundary sampling by keeping the extreme points of CDHR or -//' ix) \code{'BRDHR'} boundary sampling by keeping the extreme points of RDHR x) \code{'NUTS'} for -//' NUTS Hamiltonian Monte Carlo sampler (logconcave densities) xi) \code{'HMC'} for Hamiltonian Monte Carlo (logconcave densities) -//' xii) \code{'ULD'} for Underdamped Langevin Dynamics using the Randomized Midpoint Method (logconcave densities) +//' ii) \code{'RDHR'} for Random Directions Hit-and-Run, iii) \code{'BaW'} for Ball Walk, iv) \code{'BiW'} for Billiard walk, +//' v) \code{'dikin'} for dikin walk, vi) \code{'vaidya'} for vaidya walk, vii) \code{'john'} for john walk, +//' viii) \code{'BCDHR'} boundary sampling by keeping the extreme points of CDHR or ix) \code{'BRDHR'} boundary sampling by keeping the extreme points of RDHR, +//' x) \code{'NUTS'} for NUTS Hamiltonian Monte Carlo sampler (logconcave densities), xi) \code{'HMC'} for Hamiltonian Monte Carlo (logconcave densities), +//' xii) CRHMC for Riemannian HMC with H-polytope constraints (uniform and general logconcave densities), +//' xiii) \code{'ULD'} for Underdamped Langevin Dynamics using the Randomized Midpoint Method (logconcave densities), //' xiii) \code{'ExactHMC'} for exact Hamiltonian Monte Carlo with reflections (spherical Gaussian or exponential distribution). -//' The default walk is \code{'aBiW'} for the uniform distribution, \code{'CDHR'} for the Gaussian distribution and H-polytopes -//' and \code{'BiW'} or \code{'RDHR'} for the same distributions and V-polytopes and zonotopes. \code{'NUTS'} is the default sampler for logconcave densities.} +//' The default walk is \code{'aBiW'} for the uniform distribution, \code{'CDHR'} for the Gaussian distribution and H-polytopes and +//' \code{'BiW'} or \code{'RDHR'} for the same distributions and V-polytopes and zonotopes. \code{'NUTS'} is the default sampler for logconcave densities and \code{'CRHMC'} +//' for logconcave densities with H-polytope and sparse constrainted problems.} //' \item{\code{walk_length} }{ The number of the steps per generated point for the random walk. The default value is \eqn{1}.} //' \item{\code{nburns} }{ The number of points to burn before start sampling. The default value is \eqn{1}.} //' \item{\code{starting_point} }{ A \eqn{d}-dimensional numerical vector that declares a starting point in the interior of the polytope for the random walk. The default choice is the center of the ball as that one computed by the function \code{inner_ball()}.} @@ -350,14 +357,19 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, typedef IntersectionOfVpoly InterVP; typedef Eigen::Matrix VT; typedef Eigen::Matrix MT; + typedef Eigen::SparseMatrix SpMat; + typedef constraint_problem sparse_problem; unsigned int type = Rcpp::as(P).field("type"), dim = Rcpp::as(P).field("dimension"), walkL = 1, numpoints, nburns = 0; RcppFunctor::GradientFunctor *F = NULL; RcppFunctor::FunctionFunctor *f = NULL; + RcppFunctor::HessianFunctor *h = NULL; + GaussianFunctor::GradientFunctor *G = NULL; GaussianFunctor::FunctionFunctor *g = NULL; + GaussianFunctor::HessianFunctor *hess_g = NULL; bool functor_defined = true; @@ -475,18 +487,22 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, solver = leapfrog; } else if (solver_str == "euler") { solver = euler; + } else if (solver_str == "implicit_midpoint"){ + solver = implicit_midpoint; } else { throw Rcpp::exception("Invalid ODE solver specified. Aborting."); } } else { solver = leapfrog; } - // Create functors RcppFunctor::parameters rcpp_functor_params(L_, m, eta, 2); F = new RcppFunctor::GradientFunctor(rcpp_functor_params, negative_logprob_gradient); f = new RcppFunctor::FunctionFunctor(rcpp_functor_params, negative_logprob); - + if(Rcpp::as(distribution).containsElementNamed("negative_logprob_hessian")){ + Rcpp::Function negative_logprob_hessian = Rcpp::as(distribution)["negative_logprob_hessian"]; + h = new RcppFunctor::HessianFunctor(rcpp_functor_params, negative_logprob_hessian); + } } else if (logconcave && !Rcpp::as(distribution).containsElementNamed("negative_logprob") && @@ -509,6 +525,8 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, solver = leapfrog; } else if (solver_str == "euler") { solver = euler; + } else if (solver_str == "implicit_midpoint"){ + solver = implicit_midpoint; } else { throw Rcpp::exception("Invalid ODE solver specified. Aborting."); } @@ -516,12 +534,11 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, Rcpp::warning("Solver set to leapfrog."); solver = leapfrog; } - // Create functors - GaussianFunctor::parameters gaussian_functor_params(mode, a, eta); - G = new GaussianFunctor::GradientFunctor(gaussian_functor_params); - g = new GaussianFunctor::FunctionFunctor(gaussian_functor_params); - + GaussianFunctor::parameters* gaussian_functor_params=new GaussianFunctor::parameters(mode, a, eta); + G = new GaussianFunctor::GradientFunctor(*gaussian_functor_params); + g = new GaussianFunctor::FunctionFunctor(*gaussian_functor_params); + hess_g = new GaussianFunctor::HessianFunctor(*gaussian_functor_params); } if (!random_walk.isNotNull() || !Rcpp::as(random_walk).containsElementNamed("walk")) { @@ -531,19 +548,11 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, } walk = exponential_hmc; } else if (logconcave) { - walk = nuts; + walk = (type == 5) ? crhmc : nuts; } else if (gaussian) { - if (type == 1) { - walk = cdhr; - } else { - walk = rdhr; - } + walk = (type == 1) ? cdhr : rdhr; } else { - if (type == 1) { - walk = accelarated_billiard; - } else { - walk = billiard; - } + walk = (type == 1) ? accelarated_billiard : billiard; } } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("CDHR")) == 0) { walk = cdhr; @@ -617,6 +626,16 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("ULD")) == 0) { if (!logconcave) throw Rcpp::exception("ULD is not supported for non first-order sampling"); walk = uld; + } else if(Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("CRHMC")) == 0){ + if (!logconcave) throw Rcpp::exception("CRHMC is used for logconcave sampling"); + if (type !=1 && type !=5 ) { + throw Rcpp::exception("CRHMC sampling is supported only for H-polytopes and Sparse Problems."); + } + walk =crhmc; + if(solver!=implicit_midpoint){ + Rcpp::warning("Solver set to implicit midpoint."); + } + solver = implicit_midpoint; } else { throw Rcpp::exception("Unknown walk type!"); } @@ -667,11 +686,11 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, } if (functor_defined) { sample_from_polytope(HP, type, rng, randPoints, walkL, numpoints, gaussian, a, L, c, - StartingPoint, nburns, set_L, eta, walk, F, f, solver); + StartingPoint, nburns, set_L, eta, walk, F, f, h, solver); } else { sample_from_polytope(HP, type, rng, randPoints, walkL, numpoints, gaussian, a, L, c, - StartingPoint, nburns, set_L, eta, walk, G, g, solver); + StartingPoint, nburns, set_L, eta, walk, G, g, hess_g, solver); } break; } @@ -693,7 +712,7 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, VP.shift(mode.getCoefficients()); } sample_from_polytope(VP, type, rng, randPoints, walkL, numpoints, gaussian, a, L, c, - StartingPoint, nburns, set_L, eta, walk, F, f, solver); + StartingPoint, nburns, set_L, eta, walk, F, f, h, solver); break; } case 3: { @@ -714,7 +733,7 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, ZP.shift(mode.getCoefficients()); } sample_from_polytope(ZP, type, rng, randPoints, walkL, numpoints, gaussian, a, L, c, - StartingPoint, nburns, set_L, eta, walk, F, f, solver); + StartingPoint, nburns, set_L, eta, walk, F, f, h, solver); break; } case 4: { @@ -737,9 +756,27 @@ Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, VPcVP.shift(mode.getCoefficients()); } sample_from_polytope(VPcVP, type, rng, randPoints, walkL, numpoints, gaussian, a, L, c, - StartingPoint, nburns, set_L, eta, walk, F, f, solver); + StartingPoint, nburns, set_L, eta, walk, F, f, h, solver); break; } + case 5: { + // Sparse constraint_problem + SpMat Aeq = Rcpp::as(Rcpp::as(P).field("Aeq")); + VT beq= Rcpp::as(Rcpp::as(P).field("beq")); + SpMat Aineq = Rcpp::as(Rcpp::as(P).field("Aineq")); + VT bineq= Rcpp::as(Rcpp::as(P).field("bineq")); + VT lb= Rcpp::as(Rcpp::as(P).field("lb")); + VT ub= Rcpp::as(Rcpp::as(P).field("ub")); + sparse_problem problem(dim, Aeq, beq, Aineq, bineq, lb, ub); + if(walk!=crhmc){throw Rcpp::exception("Sparse problems are supported only by the CRHMC walk.");} + if (functor_defined) { + execute_crhmc, RcppFunctor::GradientFunctor,RcppFunctor::FunctionFunctor, RcppFunctor::HessianFunctor, CRHMCWalk, 8>(problem, rng, randPoints, walkL, numpoints, nburns, F, f, h); + } + else { + execute_crhmc, GaussianFunctor::GradientFunctor,GaussianFunctor::FunctionFunctor, GaussianFunctor::HessianFunctor, CRHMCWalk, 8>(problem, rng, randPoints, walkL, numpoints, nburns, G, g, hess_g); + } + break; + } } if (numpoints % 2 == 1 && (walk == brdhr || walk == bcdhr)) numpoints--; diff --git a/R-proj/tests/testthat/test_sampling.R b/R-proj/tests/testthat/test_sampling.R index 1950eda07..d051dc70e 100644 --- a/R-proj/tests/testthat/test_sampling.R +++ b/R-proj/tests/testthat/test_sampling.R @@ -14,59 +14,74 @@ runsample <- function(P, name_string, dist){ res = 1 } return(res) - + +} + +logconcave_sample<- function(P,distribution, n_samples ,n_burns){ + if (distribution == "uniform"){ + f <- function(x) (0) + grad_f <- function(x) (0) + L=1 + m=1 + pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f, "L_" = L, "m" = m)) + return(max(psrf_univariate(pts, "interval"))) + } + else if(distribution == "gaussian"){ + pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "variance"=8)) + return(max(psrf_univariate(pts, "interval"))) + } } for (i in 1:2) { - + if (i==1) { distribution = 'gaussian' } else { distribution = 'uniform' } - + test_that("Sampling test", { P= gen_cube(10, 'H') res = runsample(P, 'H-cube10', distribution) expect_equal(res, 1) }) - + test_that("Sampling test", { P = gen_cross(10, 'H') res = runsample(P, 'H-cross10', distribution) expect_equal(res, 1) }) - + test_that("Sampling test", { P = gen_prod_simplex(5) res = runsample(P, 'H-prod_simplex_5_5', distribution) expect_equal(res, 1) }) - + test_that("Sampling test", { P = gen_prod_simplex(10) res = runsample(P, 'H-prod_simplex_10_10', distribution) expect_equal(res, 1) }) - + test_that("Sampling test", { P = gen_simplex(10, 'H') res = runsample(P, 'H-prod_simplex10', distribution) expect_equal(res, 1) }) - + test_that("Sampling test", { P = gen_skinny_cube(10) res = runsample(P, 'H-skinny_cube10', distribution) expect_equal(res, 1) }) - + test_that("Sampling test", { P = gen_skinny_cube(20) res = runsample(P, 'H-skinny_cube20', distribution) expect_equal(res, 1) }) - + test_that("Sampling test", { Z = gen_rand_zonotope(4, 8) res = runsample(Z, 'zonotope_4_8', distribution) diff --git a/cran_gen/Makevars b/cran_gen/Makevars index a609d3dac..28c42dcad 100644 --- a/cran_gen/Makevars +++ b/cran_gen/Makevars @@ -2,11 +2,16 @@ PKG_CPPFLAGS=-Iexternal -Iexternal/lpsolve/headers/run_headers -Iexternal/minimu PKG_CXXFLAGS= -DBOOST_NO_AUTO_PTR -DDISABLE_NLP_ORACLES CXX_STD = CXX11 -PKG_LIBS=-Lexternal/lpsolve/build/lp_solve -llp_solve $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) +PKG_LIBS=-Lexternal/lpsolve/build/lp_solve -llp_solve -Lexternal/PackedCSparse/qd -lqd $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) -$(SHLIB): external/lpsolve/build/lp_solve/liblp_solve.a +$(SHLIB): external/lpsolve/build/lp_solve/liblp_solve.a external/PackedCSparse/qd/libqd.a external/lpsolve/build/lp_solve/liblp_solve.a: @(cd external/lpsolve/build/lp_solve && $(MAKE) liblp_solve.a \ CC="$(CC)" CPPFLAGS="$(CPPFLAGS)" CFLAGS="$(CFLAGS)" \ CPICFLAGS="$(CPICFLAGS)" AR="$(AR)" RANLIB="$(RANLIB)") + +external/PackedCSparse/qd/libqd.a: + @(cd external/PackedCSparse/qd/ && $(MAKE) libqd.a \ + CC="$(CC)" CPPFLAGS="$(CPPFLAGS)" CFLAGS="$(CFLAGS)" \ + CPICFLAGS="$(CPICFLAGS)" AR="$(AR)") diff --git a/cran_gen/Makevars.win b/cran_gen/Makevars.win index 70005148e..b93299131 100644 --- a/cran_gen/Makevars.win +++ b/cran_gen/Makevars.win @@ -2,12 +2,17 @@ PKG_CPPFLAGS=-Iexternal -Iexternal/lpsolve/headers/run_headers -Iexternal/minimu PKG_CXXFLAGS= -lm -ldl -DBOOST_NO_AUTO_PTR -DDISABLE_NLP_ORACLES CXX_STD = CXX11 -PKG_LIBS=-Lexternal/lpsolve/build/lp_solve -llp_solve $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) +PKG_LIBS=-Lexternal/lpsolve/build/lp_solve -llp_solve -Lexternal/PackedCSparse/qd -lqd $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) -$(SHLIB): external/lpsolve/build/lp_solve/liblp_solve.a +$(SHLIB): external/lpsolve/build/lp_solve/liblp_solve.a external/PackedCSparse/qd/libqd.a external/lpsolve/build/lp_solve/liblp_solve.a: @(cd external/lpsolve/build/lp_solve && $(MAKE) liblp_solve.a \ CC="$(CC)" CPPFLAGS="$(CPPFLAGS) -DUSRDLL -DINLINE=static" \ CFLAGS="$(CFLAGS)" CPICFLAGS="$(CPICFLAGS)" AR="$(AR)" \ RANLIB="$(RANLIB)") + +external/PackedCSparse/qd/libqd.a: + @(cd external/PackedCSparse/qd/ && $(MAKE) libqd.a \ + CC="$(CC)" CPPFLAGS="$(CPPFLAGS)" CFLAGS="$(CFLAGS)" \ + CPICFLAGS="$(CPICFLAGS)" AR="$(AR)") diff --git a/cran_gen/genCRANpkg.R b/cran_gen/genCRANpkg.R index e4f6008be..abf51d88a 100644 --- a/cran_gen/genCRANpkg.R +++ b/cran_gen/genCRANpkg.R @@ -17,17 +17,17 @@ file.copy(dir_lp, lp_dist, recursive=TRUE) # fix ftime deprecation, taken from: https://github.com/GeomScale/volesti/pull/89/files library(xfun) gsub_file( - paste0(path,"/R-proj/src/Rproj_externals/lp_solve/commonlib.c"), - "struct timeb buf;", "", + paste0(path,"/R-proj/src/Rproj_externals/lp_solve/commonlib.c"), + "struct timeb buf;", "", fixed=TRUE) gsub_file( - paste0(path,"/R-proj/src/Rproj_externals/lp_solve/commonlib.c"), - "ftime(&buf);", "", + paste0(path,"/R-proj/src/Rproj_externals/lp_solve/commonlib.c"), + "ftime(&buf);", "", fixed=TRUE) gsub_file( - paste0(path,"/R-proj/src/Rproj_externals/lp_solve/commonlib.c"), - "return((double)buf.time+((double) buf.millitm)/1000.0);", - "return((double)0);", + paste0(path,"/R-proj/src/Rproj_externals/lp_solve/commonlib.c"), + "return((double)buf.time+((double) buf.millitm)/1000.0);", + "return((double)0);", fixed=TRUE) # add lpsolve header files in external @@ -166,6 +166,8 @@ makefile_dir = paste0(path,'/cran_gen/Makefile') makefile_dist = paste0(path, '/cran_gen/cran_package/src/external/lpsolve/build/lp_solve') file.copy(makefile_dir, makefile_dist, recursive=TRUE) + + # set new cran package folder as wrking directory setwd(paste0(path,'/cran_gen/cran_package')) # enable devtools and Rcpp libraries diff --git a/examples/crhmc_prepare/CMakeLists.txt b/examples/crhmc_prepare/CMakeLists.txt index 6bdf831b2..35c31e9e1 100644 --- a/examples/crhmc_prepare/CMakeLists.txt +++ b/examples/crhmc_prepare/CMakeLists.txt @@ -121,7 +121,7 @@ else () add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-ldl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-DBOOST_NO_AUTO_PTR") - add_executable (crhmc_prepare crhmc_prepare.cpp) - TARGET_LINK_LIBRARIES(crhmc_prepare ${QD_LIB} ${MKL_LINK} ${LP_SOLVE}) + add_executable (crhmc_prepare crhmc_prepare.cpp ) + TARGET_LINK_LIBRARIES(crhmc_prepare QD_LIB ${MKL_LINK} ${LP_SOLVE}) endif() diff --git a/examples/crhmc_sampling/.gitignore b/examples/crhmc_sampling/.gitignore index 5ba563ce6..60b43389a 100644 --- a/examples/crhmc_sampling/.gitignore +++ b/examples/crhmc_sampling/.gitignore @@ -1,3 +1,7 @@ crhmc_sampling +crhmc_sample_sparse samples.txt +CRHMC_SIMD_* +sampling_functions +simple_crhmc libQD_LIB.a diff --git a/examples/crhmc_sampling/CMakeLists.txt b/examples/crhmc_sampling/CMakeLists.txt index bb206ea49..91fe46a7f 100644 --- a/examples/crhmc_sampling/CMakeLists.txt +++ b/examples/crhmc_sampling/CMakeLists.txt @@ -114,15 +114,16 @@ else () add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard - #add_definitions(${CMAKE_CXX_FLAGS} "-g") # enable debuger set(ADDITIONAL_FLAGS "-march=native -DSIMD_LEN=0 -DTIME_KEEPING") - add_definitions(${CMAKE_CXX_FLAGS} "-O3 " ${ADDITIONAL_FLAGS}) # optimization of the compiler + add_definitions(${CMAKE_CXX_FLAGS} "-O3 -DTIME_KEEPING" ${ADDITIONAL_FLAGS}) # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-ldl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-DBOOST_NO_AUTO_PTR") - add_executable (crhmc_sampling crhmc_sampling.cpp) - TARGET_LINK_LIBRARIES(crhmc_sampling ${QD_LIB} ${MKL_LINK} ${LP_SOLVE}) + add_executable (sampling_functions sampling_functions.cpp) + TARGET_LINK_LIBRARIES(sampling_functions QD_LIB ${MKL_LINK} ${LP_SOLVE}) + add_executable (simple_crhmc simple_crhmc.cpp) + TARGET_LINK_LIBRARIES(simple_crhmc QD_LIB ${MKL_LINK} ${LP_SOLVE}) endif() diff --git a/examples/crhmc_sampling/README.md b/examples/crhmc_sampling/README.md index 38070b2d3..e70ada760 100644 --- a/examples/crhmc_sampling/README.md +++ b/examples/crhmc_sampling/README.md @@ -11,11 +11,16 @@ Build the example by running the following commands in this directory. cmake . -DLP_SOLVE=_PATH_TO_LIB_FILE make ``` -You have to specify the path to liblpsolve55.so/dll/dylib. +You have to specify the path to liblpsolve55.so. For example: -DLP_SOLVE=/usr/lib/lpsolve/liblpsolve55.so ## Running: ```bash - ./crhmc_sampling >samples.txt - python3 ../python_utilities/plot_samples.py -#include -#include -#include -#include -#include -#include - -#include "Eigen/Eigen" -#include "cartesian_geom/cartesian_kernel.h" -#include "diagnostics/multivariate_psrf.hpp" -#include "ode_solvers/ode_solvers.hpp" -#include "preprocess/crhmc/crhmc_input.h" -#include "preprocess/crhmc/crhmc_problem.h" -#include "random.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_int.hpp" -#include "random/uniform_real_distribution.hpp" -#include "random_walks/random_walks.hpp" -#include "ode_solvers/oracle_functors.hpp" - -template -void run_main(int n_samples = 10000, int n_burns = -1, int dimension = 2, - int walk_length = 1, int burn_steps = 1) { - using Kernel = Cartesian; - using Point = typename Kernel::Point; - using MT = Eigen::Matrix; - using VT = Eigen::Matrix; - using RandomNumberGenerator = BoostRandomNumberGenerator; - using Func = GaussianFunctor::FunctionFunctor; - using Grad = GaussianFunctor::GradientFunctor; - using Hess = GaussianFunctor::HessianFunctor; - using func_params = GaussianFunctor::parameters; - using Input = crhmc_input; - using CrhmcProblem = crhmc_problem; - using Solver = ImplicitMidpointODESolver; - using Opts = opts; - using Hpolytope = HPolytope; - - func_params params = func_params(Point(dimension), 4, 1); - Func f(params); - Grad g(params); - Hess h(params); - if (n_burns == -1) { - n_burns = n_samples / 2; - } - RandomNumberGenerator rng(1); - unsigned int dim = dimension; - Opts options; - - CRHMCWalk::parameters crhmc_params(g, dim, options); - Input input = Input(dim, f, g, h); - input.lb = -VT::Ones(dim); - input.ub = VT::Ones(dim); - CrhmcProblem P = CrhmcProblem(input, options); - P.print(); - Point x0 = Point(P.center); - crhmc_params.eta = 0.2; - crhmc_params.momentum = 0.8; - CRHMCWalk::Walk - crhmc(P, x0, g, f, crhmc_params); - MT samples = MT(dim, n_samples - n_burns); -#ifdef TIME_KEEPING - std::chrono::time_point start, end; - start = std::chrono::high_resolution_clock::now(); -#endif -#ifdef TIME_KEEPING - std::chrono::time_point start_file, - end_file; - std::chrono::duration total_time_file = - std::chrono::duration::zero(); -#endif - int j = 0; - for (int i = 0; i < n_samples; i++) { - if (i % 1000 == 0) { - std::cerr << i << " out of " << n_samples << "\n"; - } - for (int k = 0; k < burn_steps; k++) { - crhmc.apply(rng, walk_length, true); - } -#ifdef TIME_KEEPING - start_file = std::chrono::high_resolution_clock::now(); -#endif - if (i >= n_burns) { - VT sample = crhmc.getPoint().getCoefficients(); - samples.col(j) = VT(sample); - j++; - } -#ifdef TIME_KEEPING - end_file = std::chrono::high_resolution_clock::now(); - total_time_file += end_file - start_file; -#endif - } - std::cerr << "\n"; -#ifdef TIME_KEEPING - end = std::chrono::high_resolution_clock::now(); - std::chrono::duration total_time = end - start; - std::cerr << "Total time: " << total_time.count() << "\n"; - crhmc.print_timing_information(); -#endif - - std::cerr << "Step size (final): " << crhmc.solver->eta << std::endl; - std::cerr << "Discard Ratio: " << crhmc.discard_ratio << std::endl; - std::cerr << "Average Acceptance Probability: " - << crhmc.average_acceptance_prob << std::endl; - std::cerr << "PSRF: " << multivariate_psrf(samples) << std::endl; -#ifdef TIME_KEEPING - start_file = std::chrono::high_resolution_clock::now(); -#endif - std::cerr << "Writing samples in a file \n"; - std::cout << samples.transpose() << std::endl; -#ifdef TIME_KEEPING - end_file = std::chrono::high_resolution_clock::now(); - total_time_file += end_file - start_file; - std::cerr << "Time for writing the file: " << total_time_file.count() << "\n"; -#endif -} - -int main(int argc, char *argv[]) { - std::cerr << "Example Usage: ./crhmc_sampling n_sample initial_burns " - "dimension ode_steps steps_bettween_samples\n"; - std::cerr << "Example Usage: ./crhmc_sampling 10000 5000 " - "2 1 1\n"; - if (argc == 1) - run_main(); - else if (argc == 2) - run_main(atoi(argv[1])); - else if (argc == 3) - run_main(atoi(argv[1]), atoi(argv[2])); - else if (argc == 4) - run_main(atoi(argv[1]), atoi(argv[2]), atoi(argv[3])); - else if (argc == 5) - run_main(atoi(argv[1]), atoi(argv[2]), atoi(argv[3]), - atoi(argv[4])); - return 0; -} diff --git a/examples/crhmc_sampling/data/afiro.mm b/examples/crhmc_sampling/data/afiro.mm new file mode 100644 index 000000000..28d2ef31c --- /dev/null +++ b/examples/crhmc_sampling/data/afiro.mm @@ -0,0 +1,112 @@ +%%MatrixMarket matrix coordinate real general +% Generated 25-Sep-2022 +27 52 109 +3 1 1 +4 2 1 +7 3 1 +8 4 1 +9 5 1 +10 6 1 +13 7 1 +14 8 1 +17 9 1 +18 10 1 +19 11 1 +20 12 1 +21 13 1 +22 14 1 +23 15 1 +24 16 1 +25 17 1 +26 18 1 +27 19 1 +1 20 -1 +2 20 -1.06 +3 20 1 +24 20 0.301 +1 21 1 +4 21 -1 +1 22 1 +22 22 -1 +2 23 1 +26 23 1 +5 24 -1 +6 24 -1.06 +7 24 1 +25 24 0.301 +5 25 -1 +6 25 -1.06 +8 25 1 +25 25 0.313 +5 26 -1 +6 26 -0.96 +9 26 1 +25 26 0.313 +5 27 -1 +6 27 -0.86 +10 27 1 +25 27 0.326 +7 28 -1 +21 28 2.364 +8 29 -1 +21 29 2.386 +9 30 -1 +21 30 2.408 +10 31 -1 +21 31 2.429 +4 32 1.4 +5 32 1 +5 33 1 +23 33 -1 +6 34 1 +27 34 1 +11 35 -1 +12 35 -0.43 +13 35 1 +22 35 0.109 +11 36 1 +14 36 -1 +11 37 1 +24 37 -1 +11 38 1 +21 38 -1 +12 39 1 +26 39 1 +15 40 -0.43 +16 40 1 +17 40 1 +23 40 0.109 +15 41 -0.43 +16 41 1 +18 41 1 +23 41 0.108 +15 42 -0.39 +16 42 1 +19 42 1 +23 42 0.108 +15 43 -0.37 +16 43 1 +20 43 1 +23 43 0.107 +17 44 -1 +21 44 2.191 +18 45 -1 +21 45 2.219 +19 46 -1 +21 46 2.249 +20 47 -1 +21 47 2.279 +14 48 1.4 +16 48 -1 +16 49 1 +25 49 -1 +15 50 1 +27 50 1 +16 51 1 +3 52 80 +7 52 80 +13 52 500 +16 52 44 +17 52 500 +26 52 310 +27 52 300 diff --git a/examples/crhmc_sampling/data/afiro_bounds.mm b/examples/crhmc_sampling/data/afiro_bounds.mm new file mode 100644 index 000000000..7041a15ef --- /dev/null +++ b/examples/crhmc_sampling/data/afiro_bounds.mm @@ -0,0 +1,54 @@ +%%MatrixMarket matrix coordinate real general +% Generated 25-Sep-2022 +51 2 51 +1 2 1000000000 +2 2 1000000000 +3 2 1000000000 +4 2 1000000000 +5 2 1000000000 +6 2 1000000000 +7 2 1000000000 +8 2 1000000000 +9 2 1000000000 +10 2 1000000000 +11 2 1000000000 +12 2 1000000000 +13 2 1000000000 +14 2 1000000000 +15 2 1000000000 +16 2 1000000000 +17 2 1000000000 +18 2 1000000000 +19 2 1000000000 +20 2 1000000000 +21 2 1000000000 +22 2 1000000000 +23 2 1000000000 +24 2 1000000000 +25 2 1000000000 +26 2 1000000000 +27 2 1000000000 +28 2 1000000000 +29 2 1000000000 +30 2 1000000000 +31 2 1000000000 +32 2 1000000000 +33 2 1000000000 +34 2 1000000000 +35 2 1000000000 +36 2 1000000000 +37 2 1000000000 +38 2 1000000000 +39 2 1000000000 +40 2 1000000000 +41 2 1000000000 +42 2 1000000000 +43 2 1000000000 +44 2 1000000000 +45 2 1000000000 +46 2 1000000000 +47 2 1000000000 +48 2 1000000000 +49 2 1000000000 +50 2 1000000000 +51 2 1000000000 diff --git 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z81QJn2C4J#gCFyT-eYXm=)tS$U;p|RH7J*XP=%gWLY2*8;z1?5KOqVZ9@MWRa~H4VVS*$wc?NGuy%+MAXGGb2c5tc`Muk`hDtIe0({W zT8;`SIfNl*1NO?GOE7Ihsy1Ft0i&@CD(R`=-epFw!~E3Hj3|3j)D2`Cnjv-JBEY|q zOM?>Xwqx;Jm=>T6f(6(U>?L(i)?^g}?MFE|N6~l<4buST*|i9 + +inline bool exists_check(const std::string &name) { + std::ifstream f(name.c_str()); + return f.good(); +} +/*Problem on the form X=[A|b] bounds=[lb|ub] */ +template< typename SpMat, typename VT> +void load_crhmc_problem(SpMat &A, VT &b, VT &lb, VT &ub, int &dimension, + std::string problem_name) { + { + std::string fileName("./data/"); + fileName.append(problem_name); + fileName.append(".mm"); + if(!exists_check(fileName)){ + std::cerr<<"Problem does not exist.\n"; + exit(1);} + SpMat X; + loadMarket(X, fileName); + int m = X.rows(); + dimension = X.cols() - 1; + A = X.leftCols(dimension); + b = VT(X.col(dimension)); + } + { + std::string fileName("./data/"); + fileName.append(problem_name); + fileName.append("_bounds.mm"); + if(!exists_check(fileName)){ + std::cerr<<"Problem does not exist.\n"; + exit(1);} + SpMat bounds; + loadMarket(bounds, fileName); + lb = VT(bounds.col(0)); + ub = VT(bounds.col(1)); + } +} +template +NT max_interval_psrf(MT &samples) { + NT max_psrf = NT(0); + VT intv_psrf = interval_psrf(samples); + unsigned int d = intv_psrf.rows(); + for (unsigned int i = 0; i < d; i++) { + if (intv_psrf(i) > max_psrf) + max_psrf = intv_psrf(i); + } + return max_psrf; +} +template +void diagnose(MT &samples, StreamType &stream) { + unsigned int min_ess = 0; + print_diagnostics(samples, min_ess, stream); + NT max_psrf = max_interval_psrf(samples); + stream << "max_psrf: " << max_psrf << std::endl; + stream << "min ess " << min_ess << std::endl; +} + +#endif diff --git a/examples/crhmc_sampling/sampling_functions.cpp b/examples/crhmc_sampling/sampling_functions.cpp new file mode 100644 index 000000000..61242b253 --- /dev/null +++ b/examples/crhmc_sampling/sampling_functions.cpp @@ -0,0 +1,143 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2012-2020 Vissarion Fisikopoulos +// Copyright (c) 2018-2020 Apostolos Chalkis +// Copyright (c) 2022-2022 Ioannis Iakovidis + +// Contributed and/or modified by Ioannis Iakovidis, as part of Google Summer of +// Code 2022 program. + +// Licensed under GNU LGPL.3, see LICENCE file + +// References +// Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala. "Sampling with +// Riemannian Hamiltonian +// Monte Carlo in a Constrained Space" +#include "Eigen/Eigen" +#include "cartesian_geom/cartesian_kernel.h" +#include "volume/sampling_policies.hpp" +#include "ode_solvers/ode_solvers.hpp" +#include "preprocess/crhmc/crhmc_input.h" +#include "preprocess/crhmc/crhmc_problem.h" +#include "sampling/random_point_generators.hpp" +#include "sampling/sampling.hpp" +#include "misc/misc.h" +#include "random.hpp" +#include +#include "random_walks/random_walks.hpp" +#include "generators/known_polytope_generators.h" +#include "helper_functions.hpp" +using NT = double; +using Kernel = Cartesian; +using Point = typename Kernel::Point; +using Func = GaussianFunctor::FunctionFunctor; +using Grad = GaussianFunctor::GradientFunctor; +using Hess = GaussianFunctor::HessianFunctor; +using PolytopeType = HPolytope; +using MT = PolytopeType::MT; +using VT = Eigen::Matrix; +using func_params = GaussianFunctor::parameters; +using RNG = BoostRandomNumberGenerator; +template +void sample_hpoly(int n_samples = 80000, + int n_burns = 20000) { + std::string problem_name("simplex"); + std::cerr << "CRHMC on " << problem_name << "\n"; + using Input = crhmc_input; + using CrhmcProblem = crhmc_problem; + using Solver = + ImplicitMidpointODESolver; + RNG rng(1); + PolytopeType HP=generate_simplex(2,false); + int dimension = HP.dimension(); + func_params params = func_params(Point(dimension), 0.5, 1); + Func f(params); + Grad g(params); + Hess h(params); + std::list PointList; + crhmc_sampling, PolytopeType, RNG, CRHMCWalk, NT, Point, Grad, Func, Hess, Solver>( + PointList, HP, rng, 1, n_samples, n_burns, g, f, h, simdLen); + MT samples = MT(dimension, PointList.size()); + int i=0; + for (std::list::iterator it = PointList.begin(); it != PointList.end(); ++it){ + samples.col(i) = (*it).getCoefficients(); + i++; + } + std::ofstream diagnostics_stream; + diagnostics_stream.open("CRHMC_SIMD_" + std::to_string(simdLen) + "_" + + problem_name + "_diagnostics.txt"); + diagnose(samples, diagnostics_stream); + std::ofstream samples_stream; + samples_stream.open("CRHMC_SIMD_" + std::to_string(simdLen) + "_" + + problem_name + "_samples.txt"); + samples_stream << samples.transpose() << std::endl; +} + +template +void sample_sparse_problem(int n_samples = 80000, + int n_burns = 20000){ + using SpMat = Eigen::SparseMatrix; + using ConstraintProblem =constraint_problem; + std::string problem_name("simplex3"); + std::cerr << "CRHMC on " << problem_name << "\n"; + using Input = crhmc_input; + using CrhmcProblem = crhmc_problem; + using Solver = + ImplicitMidpointODESolver; + + RNG rng(1); + SpMat A; + VT b, lb, ub; + int dimension; + load_crhmc_problem(A, b, lb, ub, dimension, problem_name); + ConstraintProblem problem = ConstraintProblem(dimension); + problem.set_equality_constraints(A, b); + problem.set_bounds(lb, ub); + func_params params = func_params(Point(dimension), 0.5, 1); + Func f(params); + Grad g(params); + Hess h(params); + std::list PointList; + crhmc_sampling, ConstraintProblem, RNG, CRHMCWalk, NT, Point, Grad, Func, Hess, Solver>( + PointList, problem, rng, 1, n_samples, n_burns, g, f, h, simdLen); + MT samples = MT(dimension, PointList.size()); + int i=0; + for (std::list::iterator it = PointList.begin(); it != PointList.end(); ++it){ + samples.col(i) = (*it).getCoefficients(); + i++; + } + std::ofstream diagnostics_stream; + diagnostics_stream.open("CRHMC_SIMD_" + std::to_string(simdLen) + "_" + + problem_name + "_diagnostics.txt"); + diagnose(samples, diagnostics_stream); + std::ofstream samples_stream; + samples_stream.open("CRHMC_SIMD_" + std::to_string(simdLen) + "_" + + problem_name + "_samples.txt"); + samples_stream << samples.transpose() << std::endl; +} +template +void run_main(int n_samples = 80000, + int n_burns = 20000){ + std::cerr<<"Sampling HPolytope\n"; + sample_hpoly(n_samples, n_burns); + std::cerr<<"Sampling Sparse Problem\n"; + sample_sparse_problem(n_samples, n_burns); +} +int main(int argc, char *argv[]) { + if (argc != 4) { + std::cerr << "Example Usage: ./crhmc_sample_sparse " + "[simdLen] [n_samples] [n_burns]\n"; + std::cerr << "i.e.: ./crhmc_sample_ 4 1000 500\n"; + exit(1); + } + if (atoi(argv[1]) == 1) { + run_main<1>(atoi(argv[2]), atoi(argv[3])); + } else if (atoi(argv[1]) == 4) { + run_main<4>(atoi(argv[2]), atoi(argv[3])); + } else if (atoi(argv[1]) == 8) { + run_main<8>(atoi(argv[2]), atoi(argv[3])); + } else if (atoi(argv[1]) == 16) { + run_main<16>(atoi(argv[2]), atoi(argv[3])); + } + return 0; +} diff --git a/examples/crhmc_sampling/simple_crhmc.cpp b/examples/crhmc_sampling/simple_crhmc.cpp new file mode 100644 index 000000000..8f07065b0 --- /dev/null +++ b/examples/crhmc_sampling/simple_crhmc.cpp @@ -0,0 +1,94 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2012-2020 Vissarion Fisikopoulos +// Copyright (c) 2018-2020 Apostolos Chalkis +// Copyright (c) 2022-2022 Ioannis Iakovidis + +// Contributed and/or modified by Ioannis Iakovidis, as part of Google Summer of +// Code 2022 program. + +// Licensed under GNU LGPL.3, see LICENCE file + +// References +// Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala. "Sampling with +// Riemannian Hamiltonian +// Monte Carlo in a Constrained Space" +#include "Eigen/Eigen" +#include "cartesian_geom/cartesian_kernel.h" +#include "volume/sampling_policies.hpp" +#include "ode_solvers/ode_solvers.hpp" +#include "preprocess/crhmc/crhmc_input.h" +#include "preprocess/crhmc/crhmc_problem.h" +#include "sampling/random_point_generators.hpp" +#include "sampling/sampling.hpp" +#include "misc/misc.h" +#include "random.hpp" +#include +#include "random_walks/random_walks.hpp" +#include "generators/known_polytope_generators.h" +#include "helper_functions.hpp" + +template +void sample_hpoly(int n_samples = 80000, + int n_burns = 20000, int dim = 2) { + using NT = double; + using Kernel = Cartesian; + using Point = typename Kernel::Point; + using Func = ZeroScalarFunctor; + using Grad = ZeroFunctor; + using Hess = ZeroFunctor; + using PolytopeType = HPolytope; + using VT = Eigen::Matrix; + using MT = PolytopeType::MT; + using RNG = BoostRandomNumberGenerator; + std::string problem_name("simplex"); + std::cerr << "CRHMC on " << problem_name << "\n"; + RNG rng(1); + PolytopeType HP=generate_simplex(dim,false); + int dimension = HP.dimension(); + Func * f = new Func; + Grad * g = new Grad; + std::list PointList; + execute_crhmc< PolytopeType, RNG, std::list, Grad, Func, Hess, CRHMCWalk, simdLen>( + HP, rng, PointList, 1, n_samples, n_burns, g, f); + MT samples = MT(dimension, PointList.size()); + int i=0; + for (std::list::iterator it = PointList.begin(); it != PointList.end(); ++it){ + samples.col(i) = (*it).getCoefficients(); + i++; + } + std::cerr<<"max_psrf: "<< max_interval_psrf(samples)<<"\n"; + std::ofstream samples_stream; + samples_stream.open("CRHMC_SIMD_" + std::to_string(simdLen) + "_" + + problem_name + "_samples.txt"); + samples_stream << samples.transpose() << std::endl; + delete f; + delete g; +} + +template +void run_main(int n_samples = 80000, + int n_burns = 20000, + int dimension = 2){ + std::cerr<<"Sampling HPolytope\n"; + sample_hpoly(n_samples, n_burns, dimension); +} +int main(int argc, char *argv[]) { + if (argc != 5) { + std::cerr << "Example Usage: ./simple_crhmc " + "[simdLen] [n_samples] [n_burns] [dimension]\n"; + std::cerr << "i.e.: ./simple_crhmc 4 1000 500 2\n"; + exit(1); + } + std::cerr << "To plot: python3 ../python_utilities/plot_samples.py (atoi(argv[2]), atoi(argv[3]), atoi(argv[4])); + } else if (atoi(argv[1]) == 4) { + run_main<4>(atoi(argv[2]), atoi(argv[3]), atoi(argv[4])); + } else if (atoi(argv[1]) == 8) { + run_main<8>(atoi(argv[2]), atoi(argv[3]), atoi(argv[4])); + } else if (atoi(argv[1]) == 16) { + run_main<16>(atoi(argv[2]), atoi(argv[3]), atoi(argv[4])); + } + return 0; +} diff --git a/examples/optimization_spectrahedra/readWriteSdpaFile.cpp b/examples/optimization_spectrahedra/readWriteSdpaFile.cpp deleted file mode 100644 index c734ccac5..000000000 --- a/examples/optimization_spectrahedra/readWriteSdpaFile.cpp +++ /dev/null @@ -1,66 +0,0 @@ -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2018 Vissarion Fisikopoulos -// Copyright (c) 2018 Apostolos Chalkis - -//Contributed and/or modified by Repouskos Panagiotis, as part of Google Summer of Code 2019 program. - -// Licensed under GNU LGPL.3, see LICENCE file - -// This examples illustrates how to read and write SDPA format files. -// It will read a semidefinite program from data/sdp_n2m3.txt, print it and then write it to a new file - -#define VOLESTI_DEBUG -#include "Eigen/Eigen" -#include "vector" -#include -#include "cartesian_geom/cartesian_kernel.h" -#include "random.hpp" -#include "spectrahedron.h" -#include "SDPAFormatManager.h" -#include "string" -#include "iostream" - -typedef double NT; -typedef Eigen::Matrix VT; -typedef Eigen::Matrix MT; -typedef Cartesian Kernel; -typedef typename Kernel::Point Point; -typedef Spectrahedron SPECTRAHEDRON; - - -int main(int argc, char* argv[]) { - std::string fileName("data/sdp_n2m3.txt"); - std::string outputFile("new_sdp_n2m3.txt"); - - SPECTRAHEDRON spectrahedron; - Point objFunction; - - // read the semidefinite program - // and create a vector (objective function) and a spectrahedron - - // open a stream to read the input file - std::ifstream in; - in.open(fileName, std::ifstream::in); - - // read the file - SdpaFormatManager sdpaFormatManager; - sdpaFormatManager.loadSDPAFormatFile(in, spectrahedron, objFunction); - - // print the contents - std::cout << "The objective Function:\n\n"; - objFunction.print(); - std::cout << "\n\nThe matrices of the spectrahedron:\n\n"; - spectrahedron.getLMI().print(); - - // open a stream to an output file - std::filebuf fb; - fb.open(outputFile, std::ios::out); - std::ostream os(&fb); - - // write a SDPA format file using the data we read before - sdpaFormatManager.writeSDPAFormatFile(os, spectrahedron, objFunction); - - return 0; -} - diff --git a/external/PackedCSparse/PackedChol.h b/external/PackedCSparse/PackedChol.h index 57a2b9232..97d102206 100644 --- a/external/PackedCSparse/PackedChol.h +++ b/external/PackedCSparse/PackedChol.h @@ -14,7 +14,7 @@ #include "leverage.h" #include "leverageJL.h" #include "multiply.h" -#include "dd_real.h" +#include "qd/dd_real.h" #include #include using namespace PackedCSparse; diff --git a/external/PackedCSparse/qd/COPYING b/external/PackedCSparse/qd/COPYING new file mode 100644 index 000000000..a20ad70eb --- /dev/null +++ b/external/PackedCSparse/qd/COPYING @@ -0,0 +1,16 @@ +This work was supported by the Director, Office of Science, Division +of Mathematical, Information, and Computational Sciences of the +U.S. Department of Energy under contract numbers DE-AC03-76SF00098 and +DE-AC02-05CH11231. + +Copyright (c) 2003-2009, The Regents of the University of California, +through Lawrence Berkeley National Laboratory (subject to receipt of +any required approvals from U.S. Dept. of Energy) All rights reserved. + +By downloading or using this software you are agreeing to the modified +BSD license that is in file "BSD-LBNL-License.doc" in the main ARPREC +directory. If you wish to use the software for commercial purposes +please contact the Technology Transfer Department at TTD@lbl.gov or +call 510-286-6457." + + diff --git a/external/PackedCSparse/qd/Makefile b/external/PackedCSparse/qd/Makefile new file mode 100644 index 000000000..791e92b99 --- /dev/null +++ b/external/PackedCSparse/qd/Makefile @@ -0,0 +1,15 @@ +QD_CPPFLAGS=$(CPPFLAGS) -I$(R_INCLUDE_DIR) -march=native + +QD_SOURCES= bits.cc c_dd.cc c_qd.cc dd_const.cc dd_real.cc fpu.cc \ + qd_const.cc qd_real.cc util.cc + +QD_OBJECTS=$(QD_SOURCES:.cc=.o) + +libqd.a: $(QD_OBJECTS) + $(AR) rc libqd.a $(QD_OBJECTS) + +.cc.o: + $(CC) $(CFLAGS) $(CPICFLAGS) $(QD_CPPFLAGS) -c $< -o $@ + +clean: + rm -rf $(QD_OBJECTS) libqd.a diff --git a/external/PackedCSparse/qd/NEWS b/external/PackedCSparse/qd/NEWS new file mode 100644 index 000000000..f32a75751 --- /dev/null +++ b/external/PackedCSparse/qd/NEWS @@ -0,0 +1,181 @@ +Changes for 2.3.22 + Made changes suggested by Vasiliy Sotnikov + +Changes for 2.3.21 + Changed renorm in include/qd/qd_inline.h + +Changes for 2.3.20 + added #include to quadt_test.cpp + changed references to 2.3.20 from 2.3.18 + +Changes for 2.3.19 + - Updated qd_real.cpp and dd_real.cpp to fix a buffer overflow problem. + +Changes for 2.3.18 + - Updated qd_real.cpp and dd_real.cpp to fix a problem in output. + +Changes for 2.3.17 + - updated qd_real.cpp, to fix a problem with improper treatment of + negative arguments in nroot. + +Changes for 2.3.16 + - Updated dd_real.cpp, to fix a problem with inaccurate values of + tanh for small arguments. + +Changes for 2.3.15 + - Updated qd_real.cpp, to fix a problem with static definitions. + +Changes for 2.3.14 + - Updated autoconfig (replaced config.sub and config.guess) + +Changes for 2.3.7 + - Fixed bug in to_digits where digits larger than 10 + where output occasionally. + +Changes for 2.3.6 + - Added fmod (C++) and mod (Fortran) functions. + +Changes for 2.3.5 + - Fixed bug in division of qd_real by dd_real. + - Fixed bug in ddoutc (Fortran ddmod.f). + - Now compiles with g++ 4.3. + - Distribute tests/coeff.dat. + +Changes for 2.3.4 + - Fixed bug in Makefile for cygwin / mingw systems. + +Changes for 2.3.3 + - Fixed bug in atan2. + +Changes for 2.3.2 + - Fixed bug in sin / cos / sincos where too much accuracy was + lost for (moderately) large angles. + - Use fused-multiply add intrinsics on IA-64 platforms if + compiled by Intel compiler. + - Fixed bug in c_dd_write and c_qd_write. + - Fixed bug were qdext.mod was not being installed. + +Changes for 2.3.1 + - Fixed bug in sincos and cos_taylor. This affected the result + of trigonometric functions in some cases. + +Changes for 2.3.0 + This is a fairly significant change, breaking API compatibility. + - Moved C++ main entry in libqdmod.a to libqd_f_main.a. + This allows to link Fortran code using QD with custom + C++ main function. Pure Fortran code will need to be linked + with qd_f_main library in addition to qdmod and qd library. + - Constructors accepting pointers made explicit. + - Fortran routines labeled as elemental or pure, where appropriate. + - Write() is now to_string(), and now takes a single fmtflag. + - dd_real addition and multiplication made commutative. + - dd_real now represented as array of two doubles, instead of + two discrete scalars. + - New Fortran generic routines to read / write, operations with + complex and integers. + - Improved exp, sin, and cos functions. + - Removed unused constants and obscure constants only used internally + from public interface. + +Changes for 2.2.6 + - Fixed bug in mixed precision multiplication: qd_real * dd_real. + +Changes for 2.2.5 + - Bug fix in qd_real addition when --enable-ieee-add is specified. + - Debugging routines dump and dump_bits updated; + dump_components removed (just use dump). + - Fortran support for Fortran strings. Use character arrays instead. + - Return NaN under error conditions. + - Added _inf constant; exp now returns Inf when argument is too large. + - Output formatting fixes for Inf and NaNs. + - Added more real-complex mixed arithmetic routines in Fortran + interface. + +Changes for 2.2.4 + - Added random_number interface for Fortran modules. + - Use slightly more conservative values for eps. + - Avoid unnecessary overflow near overflow threshold. + - Added radix, digits, min/maxexponent, range, and precision + intrinsics to Fortran interface. + - Added safe_max (C++) and safe_huge (Fortran). + +Changes for 2.2.3 + - Fix sign function bug in Fortran modules. + +Changes for 2.2.2 + - Do not bother setting uninitialized dd_real and qd_reals to zero. + - Use clock_gettime if available for timing. + - Fortran I/O should be more consistent with C++ version. + - fpu.h is now included with dd_real.h. + +Changes for 2.2.1 + - Minor fixes when printing in scientific format. + - Change search order of C++ compilers in Apple systems to avoid + case insensitive filesystems. + +Changes for 2.2.0 + - Added F95 interface for complex types. + - Renamed dd.h and qd.h to dd_real.h and qd_real.h, respectively. + This will break older C++ code using 2.1.x library, but it was + conflicting with QuickDraw libraries on Macs. (Hence the version + bump to 2.2). + - Removed overloaded typecast operators for int and double. These + permitted *automatic* conversion of dd_real/qd_real to double or + int, which is somewhat dangerous. Instead to_int and to_double + routines are added. + +Changes for 2.1.214 + - Updated pslq_test. + - Implmented numeric_limits<>. + - Better polyroot. + - Added isnan, isfinite, isinf functions. + - Fix / improve input output functions. + - Drop Microsoft Visual C++ 6.0 support. + - More efficient dd_real::sin. + +Changes for 2.1.213 + - Support for x86_64 platforms. + - Drop libtool support for now. + +Changes for 2.1.212 + - Support for pathCC compiler. + - Added accurate and sloppy versions of add / sub / mul / div avaialble. + - Added autodetection of fma functions. + +Changes for 2.1 (2003-12-30) + - added automake scripts. + - use libtool to compile / link and build libraries. + - supports standard installation targets (make install). + - support for Intel C++ compilers (icc / ecc). + - Fortran programs are now linked by C++ compiler. + - support for building shared library. + - minor bug fixes. + +Changes for 2.0 (2003-12-08) + - all header files are in "include/qd" directory. + - added autoconf scripts. + - added config.h and qd_config.h to store configuration information. + - renamed x86_* routines to fpu_* routines. + - added separate Fortran interface (f_* routines). + - options for sloppy multiply and sloppy divison separated. + - fixed C interface to be actually in C syntax. + - updated / added README, AUTHORS, NEWS, and LICENSE files. + - minor bug fixes. + +Changes for 1.2 (2003-12-04) + - added "dist-clean" target in Makefile + - initialize dd and qd variables to zero + - increases tolerance for qd / dd tests + - changed .cc extension to .cpp + - updated README, COPYING, and NEWS files + - added ChangeLog file + - fixed bug in '-all' flag in qd_test + - minor bug fixes + +Changes for 1.1 (2002-10-22) + - added "Changes" file (this file) + - fixed to + - fixed constant (3/4) * pi + - fixed exp(x) to return zero if x is a large negative number + - removed "docs" target in Makefile + diff --git a/external/PackedCSparse/qd/README b/external/PackedCSparse/qd/README new file mode 100644 index 000000000..8a085d72c --- /dev/null +++ b/external/PackedCSparse/qd/README @@ -0,0 +1,437 @@ +Quad Double computation package +Copyright (C) 2003-2019 +================================================ + +Revision date: 26 February 2019 + +Authors: +Yozo Hida U.C. Berkeley yozo@cs.berkeley.edu +Xiaoye S. Li Lawrence Berkeley Natl Lab xiaoye@nersc.gov +David H. Bailey Lawrence Berkeley Natl Lab dhbailey@lbl.gov + +C++ usage guide: +Alex Kaiser Lawrence Berkeley Natl Lab adkaiser@lbl.gov + +This work was supported by the Director, Office of Science, Division of Mathematical, +Information, and Computational Sciences of the U.S. Department of Energy under contract +number DE-AC02-05CH11231. + +This work was supported by the Director, Office of Science, Division of Mathematical, +Information, and Computational Sciences of the U.S. Department of Energy under contract +numbers DE-AC03-76SF00098 and DE-AC02-05CH11231. + +*** IMPORTANT NOTES: + +See the file COPYING for modified BSD license information. +See the file INSTALL for installation instructions. +See the file NEWS for recent revisions. +See the file docs/qd.pdf for additional information. + +Outline: + +I. Introduction +II. Installation of package, and linking and executing user files +III. C++ Usage +IV. Fortran Usage +V. Note on x86-Based Processors (MOST systems in use today) + + +I. Introduction + +This package provides numeric types of twice the precision of IEEE double (106 mantissa +bits, or approximately 32 decimal digits) and four times the precision of IEEE double (212 +mantissa bits, or approximately 64 decimal digits). Due to features such as operator and +function overloading, these facilities can be utilized with only minor modifications to +conventional C++ and Fortran-90 programs. + +In addition to the basic arithmetic operations (add, subtract, multiply, divide, square root), +common transcendental functions such as the exponential, logarithm, trigonometric and +hyperbolic functions are also included. A detailed description of the algorithms used is +available in the docs subdirectory (see docs/qd.pdf). An abridged version of this paper, +which was presented at the ARITH-15 conference, is also available at: + +Yozo Hida, Xiaoye S. Li and David H. Bailey, "Algorithms for quad-double precision + floating point arithmetic," 15th IEEE Symposium on Computer Arithmetic, IEEE Computer + Society, 2001, pg. 155-162, available at + https://www.davidhbailey.com/dhbpapers/arith15.pdf. + + +II. Installation of package, and linking and executing user files + +A. Directories + +There are six directories and several files in the main directory of this distribution, +described below + +src This contains the source code of the quad-double and double-double + library. This source code does not include inline functions, + which are found in the header files in the include directory. + +include This directory contains the header files. + +fortran This directory contains Fortran-90 files. + +tests This directory contains some simple (not comprehensive) tests. + +docs This directory contains a technical paper describing the algorithms. + +config This directory contains various scripts used by the configure + script and the Makefile. + +Please note that all commands refer to a Unix-type environment such as Mac OSX or Ubuntu +Linux using the bash shell. + +B. Installing and building + +To build the library, first run the included configure script by typing + + ./configure + +This script automatically generates makefiles for building the library and selects compilers +and necessary flags and libraries to include. If the user wishes to specify compilers or flags +they may use the following options. + + CXX C++ compiler to use + CXXFLAGS C++ compiler flags to use + CC C compiler to use (for C demo program) + CFLAGS C compiler flags to use (for C demo program) + FC Fortran 90 compiler + FCFLAGS Fortran 90 compiler flags to use + FCLIBS Fortran 90 libraries needed to link with C++ code. + +For example, if one is using GNU compilers, configure with: + + ./configure CXX=g++ FC=gfortran + +The Fortran and C++ compilers must produce compatible binaries. On some systems +additional flags must be included to ensure that portions of the +library are not built with 32 and 64 bit object files. For example, on +64-Bit Mac OSX 10.6 (Snow Leopard) and 10.7 (Lion) the correct +configure line using GNU compilers is: + + ./configure CXX=g++ FC=gfortran FCFLAGS=-m64 + +To build the library, simply type + + make + +and the automatically generated makefiles will build the library including archive files. + +To allow for easy linking to the library, the user may also wish to +install the archive files to a standard place. To do this type: + + make install + +This will also build the library if it has not already been built. Many systems, including Mac +and Ubuntu Linux systems, require administrator privileges to install the library at such +standard places. On such systems, one may type: + + sudo make install + +instead if one has sufficient access. + +The directory "tests" contains programs for high precision quadrature and integer-relation +detection. To build such programs, type: + + make demo + +in the "tests" directory. + +C. Linking and executing user programs + +C++ source files: + +The simplest way to link to the library is to install it to a standard place as described above, and use the -l option. For example + + g++ compileExample.cpp -o compileExample -l qd + +One can also use this method to build with make. A file called "compileExample.cpp" and the +associated makefile "makeCompileExample" illustrate the process. + +A third alternative is to use a link script. If one types "make demo" in the test +directory, the output produced gives guidance as to how to build the files. By +following the structure of the compiling commands one may copy the appropriate portions, +perhaps replacing the filename with an argument that the user can include at link time. +An example of such a script is as follows: + +g++ -DHAVE_CONFIG_H -I.. -I../include -I../include -O2 -MT $1.o -MD -MP -MF +.deps/qd_test.Tpo -c -o $1.o $1.cpp +mv -f .deps/$1.Tpo .deps/$1.Po +g++ -O2 -o $1 $1.o ../src/libqd.a -lm + +To use the link script, make it executable (by typing "chmod +x link.scr) and then type: + +./link.scr compileExample + +Note that the file extension is not included because the script handles all extensions, +expecting the source file to have the extension ".cpp". + +Fortran-90 source files: + +Similarly, a script for compiling fortran programs may be constructed as follows. +In the fortran directory, type "make quadtsq". This compiles the Fortran program +tquadts.f, links with all necessary library files, and produces the executable +"quadts". As this is being done, all flags and linked libraries are displayed. +For instance, on a 2019-era Apple Macintosh system, where the library was installed +as above with g++ for C++ and gfortran for Fortran-90, the following is output: + +gfortran -m64 -ffree-form -c -o tquadtsq.o tquadtsq.f +/bin/sh ../libtool --tag=CXX --mode=link g++ -O2 -o quadtsq tquadtsq.o second.o +libqdmod.la libqd_f_main.la ../src/libqd.la +-L/usr/local/gfortran/lib/gcc/x86_64-apple-darwin16/6.3.0 +-L/usr/local/gfortran/lib/gcc/x86_64-apple-darwin16/6.3.0/../../.. +-lgfortran -lquadmath -lm -lm + +Thus a general compile-link script is the following: + +gfortran -m64 -ffree-form -c -o $1.o $1.f90 +/bin/sh ../libtool --tag=CXX --mode=link g++ -O2 -o $1 $1.o second.o \ + libqdmod.la libqd_f_main.la ../src/libqd.la \ + -L/usr/local/gfortran/lib/gcc/x86_64-apple-darwin16/6.3.0 \ + -L/usr/local/gfortran/lib/gcc/x86_64-apple-darwin16/6.3.0/../../.. \ + -lgfortran -lquadmath -lm -lm + +Note that if the .f90 suffix is used for Fortran-90 source files, the +-ffree-form flag may be omitted, but the first line above should end with +"$1.f90" (as shown above). After forming the script, name file, "complink.scr", +and then type "chmod +x complink.scr". To use this script compile and link a +program named "prog.f90", type "./complink.scr prog". + + +III. C++ usage + +As much as possible, operator overloading is included to make basic programming as much +like using standard typed floating-point arithmetic. Changing many codes should be as +simple as changing type statements and a few other lines. + +i. Constructors + +To create dd_real and qd_real variables calculated to the proper precision, one must use +care to use the included constructors properly. Many computations in which variables are +not explicitly typed to multiple-precision may be evaluated with double-precision +arithmetic. The user must take care to ensure that this does not cause errors. In particular, +an expression such as 1.0/3.0 will be evaluated to double precision before assignment or +further arithmetic. Upon assignment to a multi-precision variable, the value will be zero +padded. This problem is serious and potentially difficult to debug. To avoid this, use the +included constructors to force arithmetic to be performed in the full precision requested. + +For a table with descriptions, please see the documentation file qd.pdf in the docs directory. + +ii. Included functions and Constants + +Supported functions include assignment operators, comparisons, arithmetic and +assignment operators, and increments for integer types. Standard C math functions such as +exponentiation, trigonometric, logarithmic, hyperbolic, exponential and rounding functions +are included. As in assignment statements, one must be careful with implied typing of +constants when using these functions. Many codes need particular conversion for the power +function, which is frequently used with constants that must be explicitly typed for multi- +precision codes. + +Many constants are included, which are global and calculated upon initialization. The +following list of constants is calculated for both the dd_real and qd_real classes separately. +Use care to select the correct value. + +For a table with descriptions, please see the included file README.pdf + +ii. Conversion of types + +Static casts may be used to convert constants between types. One may also use constructors +to return temporary multi-precision types within expressions, but should be careful, as this +will waste memory if done repeatedly. For example: + + qd_real y ; + y = sin( qd_real(4.0) / 3.0 ) ; + +C-style casts may be used, but are not recommended. Dynamic and reinterpret casts are +not supported and should be considered unreliable. Casting between multi-precision and +standard precision types can be dangerous, and care must be taken to ensure that programs +are working properly and accuracy has not degraded by use of a misplaced type-conversion. + +D. Available precision, Control of Precision Levels, + +The library provides greatly extended accuracy when compared to standard double +precision. The type dd_real provides for 106 mantissa bits, or about 32 decimal digits. The +type qd_real provides for 212 mantissa bits, or about 64 decimal digits. + +Both the dd_real and qd_real values use the exponent from the highest double-precision +word for arithmetic, and as such do not extend the total range of values available. That +means that the maximum absolute value for either data type is the same as that of double- +precision, or approximately 10^308. The precision near this range, however, is greatly +increased. + +E. I/O + +The standard I/O stream routines have been overloaded to be fully compatible with all +included data types. One may need to manually reset the precision of the stream to obtain +full output. For example, if 60 digits are desired, use: + +cout.precision(60) ; + +When reading values using cin, each input numerical value must start on a separate +line. Two formats are acceptable: + + 1. Write the full constant + 3. Mantissa e exponent + +Here are three valid examples: + + 1.1 + 3.14159 26535 89793 + 123.123123e50 + +When read using cin, these constants will be converted using full multi-precision accuracy. + + +IV. Fortran-90 Usage + +NEW (2007-01-10): The Fortran translation modules now support the complex datatypes +"dd_complex" and "qd_complex". + +Since the quad-double library is written in C++, it must be linked in with a C++ compiler (so +that C++ specific things such as static initializations are correctly handled). Thus the main +program must be written in C/C++ and call the Fortran 90 subroutine. The Fortran 90 +subroutine should be called f_main. + +Here is a sample Fortran-90 program, equivalent to the above C++ program: + + subroutine f_main + use qdmodule + implicit none + type (qd_real) a, b + a = 1.d0 + b = cos(a)**2 + sin(a)**2 - 1.d0 + call qdwrite(6, b) + stop + end subroutine + +This verifies that cos^2(1) + sin^2(1) = 1 to 64 digit accuracy. + +Most operators and generic function references, including many mixed-mode type +combinations with double-precision (ie real*8), have been overloaded (extended) to work +with double-double and quad-double data. It is important, however, that users keep in +mind the fact that expressions are evaluated strictly according to conventional Fortran +operator precedence rules. Thus some subexpressions may be evaluated only to 15-digit +accuracy. For example, with the code + + real*8 d1 + type (dd_real) t1, t2 + ... + t1 = cos (t2) + d1/3.d0 + +the expression d1/3.d0 is computed to real*8 accuracy only (about 15 digits), since both d1 +and 3.d0 have type real*8. This result is then converted to dd_real by zero extension before +being added to cos(t2). So, for example, if d1 held the value 1.d0, then the quotient d1/3.d0 +would only be accurate to 15 digits. If a fully accurate double-double quotient is required, +this should be written: + + real*8 d1 + type (dd_real) t1, t2 + ... + t1 = cos (t2) + ddreal (d1) / 3.d0 + +which forces all operations to be performed with double-double arithmetic. + +Along this line, a constant such as 1.1 appearing in an expression is evaluated only to real*4 +accuracy, and a constant such as 1.1d0 is evaluated only to real*8 accuracy (this is +according to standard Fortran conventions). If full quad-double accuracy is required, for +instance, one should write + + type (qd_real) t1 + ... + t1 = '1.1' + +The quotes enclosing 1.1 specify to the compiler that the constant is to be converted to +binary using quad-double arithmetic, before assignment to t1. Quoted constants may only +appear in assignment statements such as this. + +To link a Fortran-90 program with the C++ qd library, it is recommended to link with the +C++ compiler used to generate the library. The Fortran 90 interface (along with a C-style +main function calling f_main) is found in qdmod library. The qd-config script installed +during "make install" can be used to determine which flags to pass to compile and link your +programs: + + "qd-config --fcflags" displays compiler flags needed to compile your Fortran files. + "qd-config --fclibs" displays linker flags needed by the C++ linker to link in all the +necessary libraries. + +A sample Makefile that can be used as a template for compiling Fortran programs using +quad-double library is found in fortran/Makefile.sample. + +F90 functions defined with dd_real arguments: + Arithmetic: + - * / ** + Comparison tests: == < > <= >= /= + Others: abs, acos, aint, anint, asin, atan, atan2, cos, cosh, dble, erf, + erfc, exp, int, log, log10, max, min, mod, ddcsshf (cosh and sinh), + ddcssnf (cos and sin), ddranf (random number generator in (0,1)), + ddnrtf (n-th root), sign, sin, sinh, sqr, sqrt, tan, tanh + +Similar functions are provided for qd_real arguments with function names qdcsshf, +qdcssnf, qdranf and qdnrtf instead of the names in the list above. + +Input and output of double-double and quad-double data is done using the special +subroutines ddread, ddwrite, qdread and qdwrite. The first argument of these subroutines +is the Fortran I/O unit number, while additional arguments (as many as needed, up to 9 +arguments) are scalar variables or array elements of the appropriate type. Example: + + integer n + type (qd_real) qda, qdb, qdc(n) + ... + call qdwrite (6, qda, qdb) + do j = 1, n + call qdwrite (6, qdc(j)) + enddo + +Each input values must be on a separate line, and may include D or E exponents. Double- +double and quad-double constants may also be specified in assignment statements by +enclosing them in quotes, as in + + ... + type (qd_real) pi + ... + pi = +"3.14159265358979323846264338327950288419716939937510582097494459230" + ... + +Sample Fortran-90 programs illustrating some of these features are provided in the f90 +subdirectory. + + +V. Note on x86-Based Processors (MOST systems in use today) + +The algorithms in this library assume IEEE double precision floating point arithmetic. Since +Intel x86 processors have extended (80-bit) floating point registers, some compilers, +albeit a declining number, may generate commands for the 80-bit instructions. The QD +library does NOT work correctly with 80-bit instructions, so if one's code does not operate +correctly, this may be the reason. To avoid such problems, the round-to-double flag must be +enabled in the control word of the FPU for this library to function properly. The following +functions contains appropriate code to facilitate manipulation of this flag. For non-x86 +systems these functions do nothing (but still exist). + +fpu_fix_start This turns on the round-to-double bit in the control word. +fpu_fix_end This restores the control flag. + +These functions must be called by the main program, as follows: + + int main() { + unsigned int old_cw; + fpu_fix_start(&old_cw); + + ... user code using quad-double library ... + + fpu_fix_end(&old_cw); + } + +A Fortran-90 example is the following: + + subroutine f_main + use qdmodule + implicit none + integer*4 old_cw + + call f_fpu_fix_start(old_cw) + + ... user code using quad-double library ... + + call f_fpu_fix_end(old_cw) + end subroutine + diff --git a/external/PackedCSparse/qd/bits.cc b/external/PackedCSparse/qd/bits.cc new file mode 100644 index 000000000..4eaf9a265 --- /dev/null +++ b/external/PackedCSparse/qd/bits.cc @@ -0,0 +1,85 @@ +/* + * src/bits.cc + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2001 + * + * Defines various routines to get / set bits of a IEEE floating point + * number. This used by the library for debugging purposes. + */ + +#include +#include +#include +#include + +#include "qd_config.h" +#include "inline.h" +#include "bits.h" + +#ifdef HAVE_IEEEFP_H +#include +#endif + +using std::setw; + +int get_double_expn(double x) { + if (x == 0.0) + return INT_MIN; + if (QD_ISINF(x) || QD_ISNAN(x)) + return INT_MAX; + + double y = std::abs(x); + int i = 0; + if (y < 1.0) { + while (y < 1.0) { + y *= 2.0; + i++; + } + return -i; + } else if (y >= 2.0) { + while (y >= 2.0) { + y *= 0.5; + i++; + } + return i; + } + return 0; +} + +void print_double_info(std::ostream &os, double x) { + std::streamsize old_prec = os.precision(19); + std::ios_base::fmtflags old_flags = os.flags(); + os << std::scientific; + + os << setw(27) << x << ' '; + if (QD_ISNAN(x) || QD_ISINF(x) || (x == 0.0)) { + os << " "; + } else { + + x = std::abs(x); + int expn = get_double_expn(x); + double d = std::ldexp(1.0, expn); + os << setw(5) << expn << " "; + for (int i = 0; i < 53; i++) { + if (x >= d) { + x -= d; + os << '1'; + } else + os << '0'; + d *= 0.5; + } + + if (x != 0.0) { + // should not happen + os << " +trailing stuff"; + } + } + + os.precision(old_prec); + os.flags(old_flags); +} + diff --git a/external/PackedCSparse/qd/bits.h b/external/PackedCSparse/qd/bits.h new file mode 100644 index 000000000..58570aac5 --- /dev/null +++ b/external/PackedCSparse/qd/bits.h @@ -0,0 +1,32 @@ +/* + * include/bits.h + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2001 + * + * This file defines various routines to get / set bits of a IEEE floating + * point number. This is used by the library for debugging purposes. + */ + +#ifndef _QD_BITS_H +#define _QD_BITS_H + +#include +#include "qd_config.h" + +/* Returns the exponent of the double precision number. + Returns INT_MIN is x is zero, and INT_MAX if x is INF or NaN. */ +int get_double_expn(double x); + +/* Prints + SIGN EXPN MANTISSA + of the given double. If x is NaN, INF, or Zero, this + prints out the strings NaN, +/- INF, and 0. */ +void print_double_info(std::ostream &os, double x); + + +#endif /* _QD_BITS_H */ + diff --git a/external/PackedCSparse/qd/c_dd.cc b/external/PackedCSparse/qd/c_dd.cc new file mode 100644 index 000000000..0a7c12ac2 --- /dev/null +++ b/external/PackedCSparse/qd/c_dd.cc @@ -0,0 +1,314 @@ +/* + * src/c_dd.cc + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2001 + * + * Contains the C wrapper functions for double-double precision arithmetic. + * This can be used from Fortran code. + */ +#include + +#include "qd_config.h" +#include "dd_real.h" +#include "c_dd.h" + +#define TO_DOUBLE_PTR(a, ptr) ptr[0] = a.x[0]; ptr[1] = a.x[1]; + +extern "C" { + +/* add */ +void c_dd_add(const double *a, const double *b, double *c) { + dd_real cc; + cc = dd_real(a) + dd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_dd_add_dd_d(const double *a, double b, double *c) { + dd_real cc; + cc = dd_real(a) + b; + TO_DOUBLE_PTR(cc, c); +} +void c_dd_add_d_dd(double a, const double *b, double *c) { + dd_real cc; + cc = a + dd_real(b); + TO_DOUBLE_PTR(cc, c); +} + + +/* sub */ +void c_dd_sub(const double *a, const double *b, double *c) { + dd_real cc; + cc = dd_real(a) - dd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_dd_sub_dd_d(const double *a, double b, double *c) { + dd_real cc; + cc = dd_real(a) - b; + TO_DOUBLE_PTR(cc, c); +} +void c_dd_sub_d_dd(double a, const double *b, double *c) { + dd_real cc; + cc = a - dd_real(b); + TO_DOUBLE_PTR(cc, c); +} + + +/* mul */ +void c_dd_mul(const double *a, const double *b, double *c) { + dd_real cc; + cc = dd_real(a) * dd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_dd_mul_dd_d(const double *a, double b, double *c) { + dd_real cc; + cc = dd_real(a) * b; + TO_DOUBLE_PTR(cc, c); +} +void c_dd_mul_d_dd(double a, const double *b, double *c) { + dd_real cc; + cc = a * dd_real(b); + TO_DOUBLE_PTR(cc, c); +} + + +/* div */ +void c_dd_div(const double *a, const double *b, double *c) { + dd_real cc; + cc = dd_real(a) / dd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_dd_div_dd_d(const double *a, double b, double *c) { + dd_real cc; + cc = dd_real(a) / b; + TO_DOUBLE_PTR(cc, c); +} +void c_dd_div_d_dd(double a, const double *b, double *c) { + dd_real cc; + cc = a / dd_real(b); + TO_DOUBLE_PTR(cc, c); +} + + +/* copy */ +void c_dd_copy(const double *a, double *b) { + b[0] = a[0]; + b[1] = a[1]; +} +void c_dd_copy_d(double a, double *b) { + b[0] = a; + b[1] = 0.0; +} + + +void c_dd_sqrt(const double *a, double *b) { + dd_real bb; + bb = sqrt(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_sqr(const double *a, double *b) { + dd_real bb; + bb = sqr(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_dd_abs(const double *a, double *b) { + dd_real bb; + bb = abs(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_dd_npwr(const double *a, int n, double *b) { + dd_real bb; + bb = npwr(dd_real(a), n); + TO_DOUBLE_PTR(bb, b); +} + +void c_dd_nroot(const double *a, int n, double *b) { + dd_real bb; + bb = nroot(dd_real(a), n); + TO_DOUBLE_PTR(bb, b); +} + +void c_dd_nint(const double *a, double *b) { + dd_real bb; + bb = nint(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_aint(const double *a, double *b) { + dd_real bb; + bb = aint(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_floor(const double *a, double *b) { + dd_real bb; + bb = floor(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_ceil(const double *a, double *b) { + dd_real bb; + bb = ceil(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_dd_log(const double *a, double *b) { + dd_real bb; + bb = log(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_log10(const double *a, double *b) { + dd_real bb; + bb = log10(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_exp(const double *a, double *b) { + dd_real bb; + bb = exp(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_dd_sin(const double *a, double *b) { + dd_real bb; + bb = sin(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_cos(const double *a, double *b) { + dd_real bb; + bb = cos(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_tan(const double *a, double *b) { + dd_real bb; + bb = tan(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_dd_asin(const double *a, double *b) { + dd_real bb; + bb = asin(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_acos(const double *a, double *b) { + dd_real bb; + bb = acos(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_atan(const double *a, double *b) { + dd_real bb; + bb = atan(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_dd_atan2(const double *a, const double *b, double *c) { + dd_real cc; + cc = atan2(dd_real(a), dd_real(b)); + TO_DOUBLE_PTR(cc, c); +} + +void c_dd_sinh(const double *a, double *b) { + dd_real bb; + bb = sinh(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_cosh(const double *a, double *b) { + dd_real bb; + bb = cosh(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_tanh(const double *a, double *b) { + dd_real bb; + bb = tanh(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_dd_asinh(const double *a, double *b) { + dd_real bb; + bb = asinh(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_acosh(const double *a, double *b) { + dd_real bb; + bb = acosh(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_dd_atanh(const double *a, double *b) { + dd_real bb; + bb = atanh(dd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_dd_sincos(const double *a, double *s, double *c) { + dd_real ss, cc; + sincos(dd_real(a), ss, cc); + TO_DOUBLE_PTR(ss, s); + TO_DOUBLE_PTR(cc, c); +} + +void c_dd_sincosh(const double *a, double *s, double *c) { + dd_real ss, cc; + sincosh(dd_real(a), ss, cc); + TO_DOUBLE_PTR(ss, s); + TO_DOUBLE_PTR(cc, c); +} + +void c_dd_read(const char *s, double *a) { + dd_real aa(s); + TO_DOUBLE_PTR(aa, a); +} + +void c_dd_swrite(const double *a, int precision, char *s, int len) { + dd_real(a).write(s, len, precision); +} + +void c_dd_write(const double *a) { + std::cout << dd_real(a).to_string(dd_real::_ndigits) << std::endl; +} + +void c_dd_neg(const double *a, double *b) { + b[0] = -a[0]; + b[1] = -a[1]; +} + +void c_dd_rand(double *a) { + dd_real aa; + aa = ddrand(); + TO_DOUBLE_PTR(aa, a); +} + +void c_dd_comp(const double *a, const double *b, int *result) { + dd_real aa(a), bb(b); + if (aa < bb) + *result = -1; + else if (aa > bb) + *result = 1; + else + *result = 0; +} + +void c_dd_comp_dd_d(const double *a, double b, int *result) { + dd_real aa(a), bb(b); + if (aa < bb) + *result = -1; + else if (aa > bb) + *result = 1; + else + *result = 0; +} + +void c_dd_comp_d_dd(double a, const double *b, int *result) { + dd_real aa(a), bb(b); + if (aa < bb) + *result = -1; + else if (aa > bb) + *result = 1; + else + *result = 0; +} + +void c_dd_pi(double *a) { + TO_DOUBLE_PTR(dd_real::_pi, a); +} + +} diff --git a/external/PackedCSparse/qd/c_dd.h b/external/PackedCSparse/qd/c_dd.h new file mode 100644 index 000000000..310162edc --- /dev/null +++ b/external/PackedCSparse/qd/c_dd.h @@ -0,0 +1,98 @@ +/* + * include/c_dd.h + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2001 + * + * Contains C wrapper function prototypes for double-double precision + * arithmetic. This can also be used from fortran code. + */ +#ifndef _QD_C_DD_H +#define _QD_C_DD_H + +#include "qd_config.h" +#include "fpu.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/* add */ +void c_dd_add(const double *a, const double *b, double *c); +void c_dd_add_d_dd(double a, const double *b, double *c); +void c_dd_add_dd_d(const double *a, double b, double *c); + +/* sub */ +void c_dd_sub(const double *a, const double *b, double *c); +void c_dd_sub_d_dd(double a, const double *b, double *c); +void c_dd_sub_dd_d(const double *a, double b, double *c); + +/* mul */ +void c_dd_mul(const double *a, const double *b, double *c); +void c_dd_mul_d_dd(double a, const double *b, double *c); +void c_dd_mul_dd_d(const double *a, double b, double *c); + +/* div */ +void c_dd_div(const double *a, const double *b, double *c); +void c_dd_div_d_dd(double a, const double *b, double *c); +void c_dd_div_dd_d(const double *a, double b, double *c); + +/* copy */ +void c_dd_copy(const double *a, double *b); +void c_dd_copy_d(double a, double *b); + +void c_dd_sqrt(const double *a, double *b); +void c_dd_sqr(const double *a, double *b); + +void c_dd_abs(const double *a, double *b); + +void c_dd_npwr(const double *a, int b, double *c); +void c_dd_nroot(const double *a, int b, double *c); + +void c_dd_nint(const double *a, double *b); +void c_dd_aint(const double *a, double *b); +void c_dd_floor(const double *a, double *b); +void c_dd_ceil(const double *a, double *b); + +void c_dd_exp(const double *a, double *b); +void c_dd_log(const double *a, double *b); +void c_dd_log10(const double *a, double *b); + +void c_dd_sin(const double *a, double *b); +void c_dd_cos(const double *a, double *b); +void c_dd_tan(const double *a, double *b); + +void c_dd_asin(const double *a, double *b); +void c_dd_acos(const double *a, double *b); +void c_dd_atan(const double *a, double *b); +void c_dd_atan2(const double *a, const double *b, double *c); + +void c_dd_sinh(const double *a, double *b); +void c_dd_cosh(const double *a, double *b); +void c_dd_tanh(const double *a, double *b); + +void c_dd_asinh(const double *a, double *b); +void c_dd_acosh(const double *a, double *b); +void c_dd_atanh(const double *a, double *b); + +void c_dd_sincos(const double *a, double *s, double *c); +void c_dd_sincosh(const double *a, double *s, double *c); + +void c_dd_read(const char *s, double *a); +void c_dd_swrite(const double *a, int precision, char *s, int len); +void c_dd_write(const double *a); +void c_dd_neg(const double *a, double *b); +void c_dd_rand(double *a); +void c_dd_comp(const double *a, const double *b, int *result); +void c_dd_comp_dd_d(const double *a, double b, int *result); +void c_dd_comp_d_dd(double a, const double *b, int *result); +void c_dd_pi(double *a); + +#ifdef __cplusplus +} +#endif + +#endif /* _QD_C_DD_H */ diff --git a/external/PackedCSparse/qd/c_qd.cc b/external/PackedCSparse/qd/c_qd.cc new file mode 100644 index 000000000..fd50e9228 --- /dev/null +++ b/external/PackedCSparse/qd/c_qd.cc @@ -0,0 +1,450 @@ +/* + * src/c_qd.cc + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2001 + * + * Contains C wrapper function for quad-double precision arithmetic. + * This can be used from fortran code. + */ +#include + +#include "qd_config.h" +#include "qd_real.h" +#include "c_qd.h" + +#define TO_DOUBLE_PTR(a, ptr) ptr[0] = a.x[0]; ptr[1] = a.x[1]; \ + ptr[2] = a.x[2]; ptr[3] = a.x[3]; + +extern "C" { + + + +/* add */ +void c_qd_add(const double *a, const double *b, double *c) { + qd_real cc; + cc = qd_real(a) + qd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_qd_add_qd_dd(const double *a, const double *b, double *c) { + qd_real cc; + cc = qd_real(a) + dd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_qd_add_dd_qd(const double *a, const double *b, double *c) { + qd_real cc; + cc = dd_real(a) + qd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_qd_add_qd_d(const double *a, double b, double *c) { + qd_real cc; + cc = qd_real(a) + b; + TO_DOUBLE_PTR(cc, c); +} +void c_qd_add_d_qd(double a, const double *b, double *c) { + qd_real cc; + cc = a + qd_real(b); + TO_DOUBLE_PTR(cc, c); +} + + + +/* sub */ +void c_qd_sub(const double *a, const double *b, double *c) { + qd_real cc; + cc = qd_real(a) - qd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_qd_sub_qd_dd(const double *a, const double *b, double *c) { + qd_real cc; + cc = qd_real(a) - dd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_qd_sub_dd_qd(const double *a, const double *b, double *c) { + qd_real cc; + cc = dd_real(a) - qd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_qd_sub_qd_d(const double *a, double b, double *c) { + qd_real cc; + cc = qd_real(a) - b; + TO_DOUBLE_PTR(cc, c); +} +void c_qd_sub_d_qd(double a, const double *b, double *c) { + qd_real cc; + cc = a - qd_real(b); + TO_DOUBLE_PTR(cc, c); +} + + + +/* mul */ +void c_qd_mul(const double *a, const double *b, double *c) { + qd_real cc; + cc = qd_real(a) * qd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_qd_mul_qd_dd(const double *a, const double *b, double *c) { + qd_real cc; + cc = qd_real(a) * dd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_qd_mul_dd_qd(const double *a, const double *b, double *c) { + qd_real cc; + cc = dd_real(a) * qd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_qd_mul_qd_d(const double *a, double b, double *c) { + qd_real cc; + cc = qd_real(a) * b; + TO_DOUBLE_PTR(cc, c); +} +void c_qd_mul_d_qd(double a, const double *b, double *c) { + qd_real cc; + cc = a * qd_real(b); + TO_DOUBLE_PTR(cc, c); +} + + + +/* div */ +void c_qd_div(const double *a, const double *b, double *c) { + qd_real cc; + cc = qd_real(a) / qd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_qd_div_qd_dd(const double *a, const double *b, double *c) { + qd_real cc; + cc = qd_real(a) / dd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_qd_div_dd_qd(const double *a, const double *b, double *c) { + qd_real cc; + cc = dd_real(a) / qd_real(b); + TO_DOUBLE_PTR(cc, c); +} +void c_qd_div_qd_d(const double *a, double b, double *c) { + qd_real cc; + cc = qd_real(a) / b; + TO_DOUBLE_PTR(cc, c); +} +void c_qd_div_d_qd(double a, const double *b, double *c) { + qd_real cc; + cc = a / qd_real(b); + TO_DOUBLE_PTR(cc, c); +} + + + + +/* selfadd */ +void c_qd_selfadd(const double *a, double *b) { + qd_real bb(b); + bb += qd_real(a); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_selfadd_dd(const double *a, double *b) { + qd_real bb(b); + bb += dd_real(a); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_selfadd_d(double a, double *b) { + qd_real bb(b); + bb += a; + TO_DOUBLE_PTR(bb, b); +} + + + +/* selfsub */ +void c_qd_selfsub(const double *a, double *b) { + qd_real bb(b); + bb -= qd_real(a); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_selfsub_dd(const double *a, double *b) { + qd_real bb(b); + bb -= dd_real(a); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_selfsub_d(double a, double *b) { + qd_real bb(b); + bb -= a; + TO_DOUBLE_PTR(bb, b); +} + + + +/* selfmul */ +void c_qd_selfmul(const double *a, double *b) { + qd_real bb(b); + bb *= qd_real(a); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_selfmul_dd(const double *a, double *b) { + qd_real bb(b); + bb *= dd_real(a); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_selfmul_d(double a, double *b) { + qd_real bb(b); + bb *= a; + TO_DOUBLE_PTR(bb, b); +} + + + +/* selfdiv */ +void c_qd_selfdiv(const double *a, double *b) { + qd_real bb(b); + bb /= qd_real(a); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_selfdiv_dd(const double *a, double *b) { + qd_real bb(b); + bb /= dd_real(a); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_selfdiv_d(double a, double *b) { + qd_real bb(b); + bb /= a; + TO_DOUBLE_PTR(bb, b); +} + + + +/* copy */ +void c_qd_copy(const double *a, double *b) { + b[0] = a[0]; + b[1] = a[1]; + b[2] = a[2]; + b[3] = a[3]; +} +void c_qd_copy_dd(const double *a, double *b) { + b[0] = a[0]; + b[1] = a[1]; + b[2] = 0.0; + b[3] = 0.0; +} +void c_qd_copy_d(double a, double *b) { + b[0] = a; + b[1] = 0.0; + b[2] = 0.0; + b[3] = 0.0; +} + + +void c_qd_sqrt(const double *a, double *b) { + qd_real bb; + bb = sqrt(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_sqr(const double *a, double *b) { + qd_real bb; + bb = sqr(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_qd_abs(const double *a, double *b) { + qd_real bb; + bb = abs(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_qd_npwr(const double *a, int n, double *b) { + qd_real bb; + bb = npwr(qd_real(a), n); + TO_DOUBLE_PTR(bb, b); +} + +void c_qd_nroot(const double *a, int n, double *b) { + qd_real bb; + bb = nroot(qd_real(a), n); + TO_DOUBLE_PTR(bb, b); +} + +void c_qd_nint(const double *a, double *b) { + qd_real bb; + bb = nint(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_aint(const double *a, double *b) { + qd_real bb; + bb = aint(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_floor(const double *a, double *b) { + qd_real bb; + bb = floor(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_ceil(const double *a, double *b) { + qd_real bb; + bb = ceil(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_qd_log(const double *a, double *b) { + qd_real bb; + bb = log(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_log10(const double *a, double *b) { + qd_real bb; + bb = log10(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_exp(const double *a, double *b) { + qd_real bb; + bb = exp(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_qd_sin(const double *a, double *b) { + qd_real bb; + bb = sin(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_cos(const double *a, double *b) { + qd_real bb; + bb = cos(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_tan(const double *a, double *b) { + qd_real bb; + bb = tan(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_qd_asin(const double *a, double *b) { + qd_real bb; + bb = asin(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_acos(const double *a, double *b) { + qd_real bb; + bb = acos(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_atan(const double *a, double *b) { + qd_real bb; + bb = atan(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_qd_atan2(const double *a, const double *b, double *c) { + qd_real cc; + cc = atan2(qd_real(a), qd_real(b)); + TO_DOUBLE_PTR(cc, c); +} + +void c_qd_sinh(const double *a, double *b) { + qd_real bb; + bb = sinh(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_cosh(const double *a, double *b) { + qd_real bb; + bb = cosh(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_tanh(const double *a, double *b) { + qd_real bb; + bb = tanh(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_qd_asinh(const double *a, double *b) { + qd_real bb; + bb = asinh(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_acosh(const double *a, double *b) { + qd_real bb; + bb = acosh(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} +void c_qd_atanh(const double *a, double *b) { + qd_real bb; + bb = atanh(qd_real(a)); + TO_DOUBLE_PTR(bb, b); +} + +void c_qd_sincos(const double *a, double *s, double *c) { + qd_real ss, cc; + sincos(qd_real(a), ss, cc); + TO_DOUBLE_PTR(cc, c); + TO_DOUBLE_PTR(ss, s); +} + +void c_qd_sincosh(const double *a, double *s, double *c) { + qd_real ss, cc; + sincosh(qd_real(a), ss, cc); + TO_DOUBLE_PTR(cc, c); + TO_DOUBLE_PTR(ss, s); +} + +void c_qd_read(const char *s, double *a) { + qd_real aa(s); + TO_DOUBLE_PTR(aa, a); +} + +void c_qd_swrite(const double *a, int precision, char *s, int len) { + qd_real(a).write(s, len, precision); +} + +void c_qd_write(const double *a) { + std::cout << qd_real(a).to_string(qd_real::_ndigits) << std::endl; +} + +void c_qd_neg(const double *a, double *b) { + b[0] = -a[0]; + b[1] = -a[1]; + b[2] = -a[2]; + b[3] = -a[3]; +} + +void c_qd_rand(double *a) { + qd_real aa; + aa = qdrand(); + TO_DOUBLE_PTR(aa, a); +} + +void c_qd_comp(const double *a, const double *b, int *result) { + qd_real aa(a), bb(b); + if (aa < bb) + *result = -1; + else if (aa > bb) + *result = 1; + else + *result = 0; +} + +void c_qd_comp_qd_d(const double *a, double b, int *result) { + qd_real aa(a); + if (aa < b) + *result = -1; + else if (aa > b) + *result = 1; + else + *result = 0; +} + +void c_qd_comp_d_qd(double a, const double *b, int *result) { + qd_real bb(b); + if (a < bb) + *result = -1; + else if (a > bb) + *result = 1; + else + *result = 0; +} + +void c_qd_pi(double *a) { + TO_DOUBLE_PTR(qd_real::_pi, a); +} + +} diff --git a/external/PackedCSparse/qd/c_qd.h b/external/PackedCSparse/qd/c_qd.h new file mode 100644 index 000000000..d11a7ff17 --- /dev/null +++ b/external/PackedCSparse/qd/c_qd.h @@ -0,0 +1,119 @@ +/* + * include/c_qd.h + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2001 + * + * Contains C wrapper function prototypes for quad-double precision + * arithmetic. This can also be used from fortran code. + */ +#ifndef _QD_C_QD_H +#define _QD_C_QD_H + +#include "c_dd.h" +#include "qd_config.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/* add */ +void c_qd_add(const double *a, const double *b, double *c); +void c_qd_add_dd_qd(const double *a, const double *b, double *c); +void c_qd_add_qd_dd(const double *a, const double *b, double *c); +void c_qd_add_d_qd(double a, const double *b, double *c); +void c_qd_add_qd_d(const double *a, double b, double *c); +void c_qd_selfadd(const double *a, double *b); +void c_qd_selfadd_dd(const double *a, double *b); +void c_qd_selfadd_d(double a, double *b); + +/* sub */ +void c_qd_sub(const double *a, const double *b, double *c); +void c_qd_sub_dd_qd(const double *a, const double *b, double *c); +void c_qd_sub_qd_dd(const double *a, const double *b, double *c); +void c_qd_sub_d_qd(double a, const double *b, double *c); +void c_qd_sub_qd_d(const double *a, double b, double *c); +void c_qd_selfsub(const double *a, double *b); +void c_qd_selfsub_dd(const double *a, double *b); +void c_qd_selfsub_d(double a, double *b); + +/* mul */ +void c_qd_mul(const double *a, const double *b, double *c); +void c_qd_mul_dd_qd(const double *a, const double *b, double *c); +void c_qd_mul_qd_dd(const double *a, const double *b, double *c); +void c_qd_mul_d_qd(double a, const double *b, double *c); +void c_qd_mul_qd_d(const double *a, double b, double *c); +void c_qd_selfmul(const double *a, double *b); +void c_qd_selfmul_dd(const double *a, double *b); +void c_qd_selfmul_d(double a, double *b); + +/* div */ +void c_qd_div(const double *a, const double *b, double *c); +void c_qd_div_dd_qd(const double *a, const double *b, double *c); +void c_qd_div_qd_dd(const double *a, const double *b, double *c); +void c_qd_div_d_qd(double a, const double *b, double *c); +void c_qd_div_qd_d(const double *a, double b, double *c); +void c_qd_selfdiv(const double *a, double *b); +void c_qd_selfdiv_dd(const double *a, double *b); +void c_qd_selfdiv_d(double a, double *b); + +/* copy */ +void c_qd_copy(const double *a, double *b); +void c_qd_copy_dd(const double *a, double *b); +void c_qd_copy_d(double a, double *b); + +void c_qd_sqrt(const double *a, double *b); +void c_qd_sqr(const double *a, double *b); + +void c_qd_abs(const double *a, double *b); + +void c_qd_npwr(const double *a, int b, double *c); +void c_qd_nroot(const double *a, int b, double *c); + +void c_qd_nint(const double *a, double *b); +void c_qd_aint(const double *a, double *b); +void c_qd_floor(const double *a, double *b); +void c_qd_ceil(const double *a, double *b); + +void c_qd_exp(const double *a, double *b); +void c_qd_log(const double *a, double *b); +void c_qd_log10(const double *a, double *b); + +void c_qd_sin(const double *a, double *b); +void c_qd_cos(const double *a, double *b); +void c_qd_tan(const double *a, double *b); + +void c_qd_asin(const double *a, double *b); +void c_qd_acos(const double *a, double *b); +void c_qd_atan(const double *a, double *b); +void c_qd_atan2(const double *a, const double *b, double *c); + +void c_qd_sinh(const double *a, double *b); +void c_qd_cosh(const double *a, double *b); +void c_qd_tanh(const double *a, double *b); + +void c_qd_asinh(const double *a, double *b); +void c_qd_acosh(const double *a, double *b); +void c_qd_atanh(const double *a, double *b); + +void c_qd_sincos(const double *a, double *s, double *c); +void c_qd_sincosh(const double *a, double *s, double *c); + +void c_qd_read(const char *s, double *a); +void c_qd_swrite(const double *a, int precision, char *s, int len); +void c_qd_write(const double *a); +void c_qd_neg(const double *a, double *b); +void c_qd_rand(double *a); +void c_qd_comp(const double *a, const double *b, int *result); +void c_qd_comp_qd_d(const double *a, double b, int *result); +void c_qd_comp_d_qd(double a, const double *b, int *result); +void c_qd_pi(double *a); + +#ifdef __cplusplus +} +#endif + +#endif /* _QD_C_QD_H */ diff --git a/external/PackedCSparse/qd/dd_const.cc b/external/PackedCSparse/qd/dd_const.cc new file mode 100644 index 000000000..38b7b5ae6 --- /dev/null +++ b/external/PackedCSparse/qd/dd_const.cc @@ -0,0 +1,40 @@ +/* + * src/dd_const.cc + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2007 + */ +#include "qd_config.h" +#include "dd_real.h" + +const dd_real dd_real::_2pi = dd_real(6.283185307179586232e+00, + 2.449293598294706414e-16); +const dd_real dd_real::_pi = dd_real(3.141592653589793116e+00, + 1.224646799147353207e-16); +const dd_real dd_real::_pi2 = dd_real(1.570796326794896558e+00, + 6.123233995736766036e-17); +const dd_real dd_real::_pi4 = dd_real(7.853981633974482790e-01, + 3.061616997868383018e-17); +const dd_real dd_real::_3pi4 = dd_real(2.356194490192344837e+00, + 9.1848509936051484375e-17); +const dd_real dd_real::_e = dd_real(2.718281828459045091e+00, + 1.445646891729250158e-16); +const dd_real dd_real::_log2 = dd_real(6.931471805599452862e-01, + 2.319046813846299558e-17); +const dd_real dd_real::_log10 = dd_real(2.302585092994045901e+00, + -2.170756223382249351e-16); +const dd_real dd_real::_nan = dd_real(qd::_d_nan, qd::_d_nan); +const dd_real dd_real::_inf = dd_real(qd::_d_inf, qd::_d_inf); + +const double dd_real::_eps = 4.93038065763132e-32; // 2^-104 +const double dd_real::_min_normalized = 2.0041683600089728e-292; // = 2^(-1022 + 53) +const dd_real dd_real::_max = + dd_real(1.79769313486231570815e+308, 9.97920154767359795037e+291); +const dd_real dd_real::_safe_max = + dd_real(1.7976931080746007281e+308, 9.97920154767359795037e+291); +const int dd_real::_ndigits = 31; + + diff --git a/external/PackedCSparse/qd/dd_inline.h b/external/PackedCSparse/qd/dd_inline.h new file mode 100644 index 000000000..89bc24f2a --- /dev/null +++ b/external/PackedCSparse/qd/dd_inline.h @@ -0,0 +1,621 @@ +/* + * include/dd_inline.h + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2001 + * + * Contains small functions (suitable for inlining) in the double-double + * arithmetic package. + */ +#ifndef _QD_DD_INLINE_H +#define _QD_DD_INLINE_H + +#include +#include "inline.h" + +#ifndef QD_INLINE +#define inline +#endif + + +/*********** Additions ************/ +/* double-double = double + double */ +inline dd_real dd_real::add(double a, double b) { + double s, e; + s = qd::two_sum(a, b, e); + return dd_real(s, e); +} + +/* double-double + double */ +inline dd_real operator+(const dd_real &a, double b) { + double s1, s2; + s1 = qd::two_sum(a.x[0], b, s2); + s2 += a.x[1]; + s1 = qd::quick_two_sum(s1, s2, s2); + return dd_real(s1, s2); +} + +/* double-double + double-double */ +inline dd_real dd_real::ieee_add(const dd_real &a, const dd_real &b) { + /* This one satisfies IEEE style error bound, + due to K. Briggs and W. Kahan. */ + double s1, s2, t1, t2; + + s1 = qd::two_sum(a.x[0], b.x[0], s2); + t1 = qd::two_sum(a.x[1], b.x[1], t2); + s2 += t1; + s1 = qd::quick_two_sum(s1, s2, s2); + s2 += t2; + s1 = qd::quick_two_sum(s1, s2, s2); + return dd_real(s1, s2); +} + +inline dd_real dd_real::sloppy_add(const dd_real &a, const dd_real &b) { + /* This is the less accurate version ... obeys Cray-style + error bound. */ + double s, e; + + s = qd::two_sum(a.x[0], b.x[0], e); + e += (a.x[1] + b.x[1]); + s = qd::quick_two_sum(s, e, e); + return dd_real(s, e); +} + +inline dd_real operator+(const dd_real &a, const dd_real &b) { +#ifndef QD_IEEE_ADD + return dd_real::sloppy_add(a, b); +#else + return dd_real::ieee_add(a, b); +#endif +} + +/* double + double-double */ +inline dd_real operator+(double a, const dd_real &b) { + return (b + a); +} + + +/*********** Self-Additions ************/ +/* double-double += double */ +inline dd_real &dd_real::operator+=(double a) { + double s1, s2; + s1 = qd::two_sum(x[0], a, s2); + s2 += x[1]; + x[0] = qd::quick_two_sum(s1, s2, x[1]); + return *this; +} + +/* double-double += double-double */ +inline dd_real &dd_real::operator+=(const dd_real &a) { +#ifndef QD_IEEE_ADD + double s, e; + s = qd::two_sum(x[0], a.x[0], e); + e += x[1]; + e += a.x[1]; + x[0] = qd::quick_two_sum(s, e, x[1]); + return *this; +#else + double s1, s2, t1, t2; + s1 = qd::two_sum(x[0], a.x[0], s2); + t1 = qd::two_sum(x[1], a.x[1], t2); + s2 += t1; + s1 = qd::quick_two_sum(s1, s2, s2); + s2 += t2; + x[0] = qd::quick_two_sum(s1, s2, x[1]); + return *this; +#endif +} + +/*********** Subtractions ************/ +/* double-double = double - double */ +inline dd_real dd_real::sub(double a, double b) { + double s, e; + s = qd::two_diff(a, b, e); + return dd_real(s, e); +} + +/* double-double - double */ +inline dd_real operator-(const dd_real &a, double b) { + double s1, s2; + s1 = qd::two_diff(a.x[0], b, s2); + s2 += a.x[1]; + s1 = qd::quick_two_sum(s1, s2, s2); + return dd_real(s1, s2); +} + +/* double-double - double-double */ +inline dd_real operator-(const dd_real &a, const dd_real &b) { +#ifndef QD_IEEE_ADD + double s, e; + s = qd::two_diff(a.x[0], b.x[0], e); + e += a.x[1]; + e -= b.x[1]; + s = qd::quick_two_sum(s, e, e); + return dd_real(s, e); +#else + double s1, s2, t1, t2; + s1 = qd::two_diff(a.x[0], b.x[0], s2); + t1 = qd::two_diff(a.x[1], b.x[1], t2); + s2 += t1; + s1 = qd::quick_two_sum(s1, s2, s2); + s2 += t2; + s1 = qd::quick_two_sum(s1, s2, s2); + return dd_real(s1, s2); +#endif +} + +/* double - double-double */ +inline dd_real operator-(double a, const dd_real &b) { + double s1, s2; + s1 = qd::two_diff(a, b.x[0], s2); + s2 -= b.x[1]; + s1 = qd::quick_two_sum(s1, s2, s2); + return dd_real(s1, s2); +} + +/*********** Self-Subtractions ************/ +/* double-double -= double */ +inline dd_real &dd_real::operator-=(double a) { + double s1, s2; + s1 = qd::two_diff(x[0], a, s2); + s2 += x[1]; + x[0] = qd::quick_two_sum(s1, s2, x[1]); + return *this; +} + +/* double-double -= double-double */ +inline dd_real &dd_real::operator-=(const dd_real &a) { +#ifndef QD_IEEE_ADD + double s, e; + s = qd::two_diff(x[0], a.x[0], e); + e += x[1]; + e -= a.x[1]; + x[0] = qd::quick_two_sum(s, e, x[1]); + return *this; +#else + double s1, s2, t1, t2; + s1 = qd::two_diff(x[0], a.x[0], s2); + t1 = qd::two_diff(x[1], a.x[1], t2); + s2 += t1; + s1 = qd::quick_two_sum(s1, s2, s2); + s2 += t2; + x[0] = qd::quick_two_sum(s1, s2, x[1]); + return *this; +#endif +} + +/*********** Unary Minus ***********/ +inline dd_real dd_real::operator-() const { + return dd_real(-x[0], -x[1]); +} + +/*********** Multiplications ************/ +/* double-double = double * double */ +inline dd_real dd_real::mul(double a, double b) { + double p, e; + p = qd::two_prod(a, b, e); + return dd_real(p, e); +} + +/* double-double * (2.0 ^ exp) */ +inline dd_real ldexp(const dd_real &a, int exp) { + return dd_real(std::ldexp(a.x[0], exp), std::ldexp(a.x[1], exp)); +} + +/* double-double * double, where double is a power of 2. */ +inline dd_real mul_pwr2(const dd_real &a, double b) { + return dd_real(a.x[0] * b, a.x[1] * b); +} + +/* double-double * double */ +inline dd_real operator*(const dd_real &a, double b) { + double p1, p2; + + p1 = qd::two_prod(a.x[0], b, p2); + p2 += (a.x[1] * b); + p1 = qd::quick_two_sum(p1, p2, p2); + return dd_real(p1, p2); +} + +/* double-double * double-double */ +inline dd_real operator*(const dd_real &a, const dd_real &b) { + double p1, p2; + + p1 = qd::two_prod(a.x[0], b.x[0], p2); + p2 += (a.x[0] * b.x[1] + a.x[1] * b.x[0]); + p1 = qd::quick_two_sum(p1, p2, p2); + return dd_real(p1, p2); +} + +/* double * double-double */ +inline dd_real operator*(double a, const dd_real &b) { + return (b * a); +} + +/*********** Self-Multiplications ************/ +/* double-double *= double */ +inline dd_real &dd_real::operator*=(double a) { + double p1, p2; + p1 = qd::two_prod(x[0], a, p2); + p2 += x[1] * a; + x[0] = qd::quick_two_sum(p1, p2, x[1]); + return *this; +} + +/* double-double *= double-double */ +inline dd_real &dd_real::operator*=(const dd_real &a) { + double p1, p2; + p1 = qd::two_prod(x[0], a.x[0], p2); + p2 += a.x[1] * x[0]; + p2 += a.x[0] * x[1]; + x[0] = qd::quick_two_sum(p1, p2, x[1]); + return *this; +} + +/*********** Divisions ************/ +inline dd_real dd_real::div(double a, double b) { + double q1, q2; + double p1, p2; + double s, e; + + q1 = a / b; + + /* Compute a - q1 * b */ + p1 = qd::two_prod(q1, b, p2); + s = qd::two_diff(a, p1, e); + e -= p2; + + /* get next approximation */ + q2 = (s + e) / b; + + s = qd::quick_two_sum(q1, q2, e); + + return dd_real(s, e); +} + +/* double-double / double */ +inline dd_real operator/(const dd_real &a, double b) { + + double q1, q2; + double p1, p2; + double s, e; + dd_real r; + + q1 = a.x[0] / b; /* approximate quotient. */ + + /* Compute this - q1 * d */ + p1 = qd::two_prod(q1, b, p2); + s = qd::two_diff(a.x[0], p1, e); + e += a.x[1]; + e -= p2; + + /* get next approximation. */ + q2 = (s + e) / b; + + /* renormalize */ + r.x[0] = qd::quick_two_sum(q1, q2, r.x[1]); + + return r; +} + +inline dd_real dd_real::sloppy_div(const dd_real &a, const dd_real &b) { + double s1, s2; + double q1, q2; + dd_real r; + + q1 = a.x[0] / b.x[0]; /* approximate quotient */ + + /* compute this - q1 * dd */ + r = b * q1; + s1 = qd::two_diff(a.x[0], r.x[0], s2); + s2 -= r.x[1]; + s2 += a.x[1]; + + /* get next approximation */ + q2 = (s1 + s2) / b.x[0]; + + /* renormalize */ + r.x[0] = qd::quick_two_sum(q1, q2, r.x[1]); + return r; +} + +inline dd_real dd_real::accurate_div(const dd_real &a, const dd_real &b) { + double q1, q2, q3; + dd_real r; + + q1 = a.x[0] / b.x[0]; /* approximate quotient */ + + r = a - q1 * b; + + q2 = r.x[0] / b.x[0]; + r -= (q2 * b); + + q3 = r.x[0] / b.x[0]; + + q1 = qd::quick_two_sum(q1, q2, q2); + r = dd_real(q1, q2) + q3; + return r; +} + +/* double-double / double-double */ +inline dd_real operator/(const dd_real &a, const dd_real &b) { +#ifdef QD_SLOPPY_DIV + return dd_real::sloppy_div(a, b); +#else + return dd_real::accurate_div(a, b); +#endif +} + +/* double / double-double */ +inline dd_real operator/(double a, const dd_real &b) { + return dd_real(a) / b; +} + +inline dd_real inv(const dd_real &a) { + return 1.0 / a; +} + +/*********** Self-Divisions ************/ +/* double-double /= double */ +inline dd_real &dd_real::operator/=(double a) { + *this = *this / a; + return *this; +} + +/* double-double /= double-double */ +inline dd_real &dd_real::operator/=(const dd_real &a) { + *this = *this / a; + return *this; +} + +/********** Remainder **********/ +inline dd_real drem(const dd_real &a, const dd_real &b) { + dd_real n = nint(a / b); + return (a - n * b); +} + +inline dd_real divrem(const dd_real &a, const dd_real &b, dd_real &r) { + dd_real n = nint(a / b); + r = a - n * b; + return n; +} + +/*********** Squaring **********/ +inline dd_real sqr(const dd_real &a) { + double p1, p2; + double s1, s2; + p1 = qd::two_sqr(a.x[0], p2); + p2 += 2.0 * a.x[0] * a.x[1]; + p2 += a.x[1] * a.x[1]; + s1 = qd::quick_two_sum(p1, p2, s2); + return dd_real(s1, s2); +} + +inline dd_real dd_real::sqr(double a) { + double p1, p2; + p1 = qd::two_sqr(a, p2); + return dd_real(p1, p2); +} + + +/********** Exponentiation **********/ +inline dd_real dd_real::operator^(int n) { + return npwr(*this, n); +} + + +/*********** Assignments ************/ +/* double-double = double */ +inline dd_real &dd_real::operator=(double a) { + x[0] = a; + x[1] = 0.0; + return *this; +} + +/*********** Equality Comparisons ************/ +/* double-double == double */ +inline bool operator==(const dd_real &a, double b) { + return (a.x[0] == b && a.x[1] == 0.0); +} + +/* double-double == double-double */ +inline bool operator==(const dd_real &a, const dd_real &b) { + return (a.x[0] == b.x[0] && a.x[1] == b.x[1]); +} + +/* double == double-double */ +inline bool operator==(double a, const dd_real &b) { + return (a == b.x[0] && b.x[1] == 0.0); +} + +/*********** Greater-Than Comparisons ************/ +/* double-double > double */ +inline bool operator>(const dd_real &a, double b) { + return (a.x[0] > b || (a.x[0] == b && a.x[1] > 0.0)); +} + +/* double-double > double-double */ +inline bool operator>(const dd_real &a, const dd_real &b) { + return (a.x[0] > b.x[0] || (a.x[0] == b.x[0] && a.x[1] > b.x[1])); +} + +/* double > double-double */ +inline bool operator>(double a, const dd_real &b) { + return (a > b.x[0] || (a == b.x[0] && b.x[1] < 0.0)); +} + +/*********** Less-Than Comparisons ************/ +/* double-double < double */ +inline bool operator<(const dd_real &a, double b) { + return (a.x[0] < b || (a.x[0] == b && a.x[1] < 0.0)); +} + +/* double-double < double-double */ +inline bool operator<(const dd_real &a, const dd_real &b) { + return (a.x[0] < b.x[0] || (a.x[0] == b.x[0] && a.x[1] < b.x[1])); +} + +/* double < double-double */ +inline bool operator<(double a, const dd_real &b) { + return (a < b.x[0] || (a == b.x[0] && b.x[1] > 0.0)); +} + +/*********** Greater-Than-Or-Equal-To Comparisons ************/ +/* double-double >= double */ +inline bool operator>=(const dd_real &a, double b) { + return (a.x[0] > b || (a.x[0] == b && a.x[1] >= 0.0)); +} + +/* double-double >= double-double */ +inline bool operator>=(const dd_real &a, const dd_real &b) { + return (a.x[0] > b.x[0] || (a.x[0] == b.x[0] && a.x[1] >= b.x[1])); +} + +/* double >= double-double */ +inline bool operator>=(double a, const dd_real &b) { + return (b <= a); +} + +/*********** Less-Than-Or-Equal-To Comparisons ************/ +/* double-double <= double */ +inline bool operator<=(const dd_real &a, double b) { + return (a.x[0] < b || (a.x[0] == b && a.x[1] <= 0.0)); +} + +/* double-double <= double-double */ +inline bool operator<=(const dd_real &a, const dd_real &b) { + return (a.x[0] < b.x[0] || (a.x[0] == b.x[0] && a.x[1] <= b.x[1])); +} + +/* double <= double-double */ +inline bool operator<=(double a, const dd_real &b) { + return (b >= a); +} + +/*********** Not-Equal-To Comparisons ************/ +/* double-double != double */ +inline bool operator!=(const dd_real &a, double b) { + return (a.x[0] != b || a.x[1] != 0.0); +} + +/* double-double != double-double */ +inline bool operator!=(const dd_real &a, const dd_real &b) { + return (a.x[0] != b.x[0] || a.x[1] != b.x[1]); +} + +/* double != double-double */ +inline bool operator!=(double a, const dd_real &b) { + return (a != b.x[0] || b.x[1] != 0.0); +} + +/*********** Micellaneous ************/ +/* this == 0 */ +inline bool dd_real::is_zero() const { + return (x[0] == 0.0); +} + +/* this == 1 */ +inline bool dd_real::is_one() const { + return (x[0] == 1.0 && x[1] == 0.0); +} + +/* this > 0 */ +inline bool dd_real::is_positive() const { + return (x[0] > 0.0); +} + +/* this < 0 */ +inline bool dd_real::is_negative() const { + return (x[0] < 0.0); +} + +inline dd_real::operator bool() const { + return (x[0] != 0.0); +} + +inline dd_real::operator double() const { + return to_double(*this); +} + +/* Absolute value */ +inline dd_real abs(const dd_real &a) { + return (a.x[0] < 0.0) ? -a : a; +} + +inline dd_real fabs(const dd_real &a) { + return abs(a); +} + +/* Round to Nearest integer */ +inline dd_real nint(const dd_real &a) { + double hi = qd::nint(a.x[0]); + double lo; + + if (hi == a.x[0]) { + /* High word is an integer already. Round the low word.*/ + lo = qd::nint(a.x[1]); + + /* Renormalize. This is needed if x[0] = some integer, x[1] = 1/2.*/ + hi = qd::quick_two_sum(hi, lo, lo); + } else { + /* High word is not an integer. */ + lo = 0.0; + if (std::abs(hi-a.x[0]) == 0.5 && a.x[1] < 0.0) { + /* There is a tie in the high word, consult the low word + to break the tie. */ + hi -= 1.0; /* NOTE: This does not cause INEXACT. */ + } + } + + return dd_real(hi, lo); +} + +inline dd_real floor(const dd_real &a) { + double hi = std::floor(a.x[0]); + double lo = 0.0; + + if (hi == a.x[0]) { + /* High word is integer already. Round the low word. */ + lo = std::floor(a.x[1]); + hi = qd::quick_two_sum(hi, lo, lo); + } + + return dd_real(hi, lo); +} + +inline dd_real ceil(const dd_real &a) { + double hi = std::ceil(a.x[0]); + double lo = 0.0; + + if (hi == a.x[0]) { + /* High word is integer already. Round the low word. */ + lo = std::ceil(a.x[1]); + hi = qd::quick_two_sum(hi, lo, lo); + } + + return dd_real(hi, lo); +} + +inline dd_real aint(const dd_real &a) { + return (a.x[0] >= 0.0) ? floor(a) : ceil(a); +} + +/* Cast to double. */ +inline double to_double(const dd_real &a) { + return a.x[0]; +} + +/* Cast to int. */ +inline int to_int(const dd_real &a) { + return static_cast(a.x[0]); +} + +/* Random number generator */ +inline dd_real dd_real::rand() { + return ddrand(); +} + +#endif /* _QD_DD_INLINE_H */ diff --git a/external/PackedCSparse/qd/dd_real.cc b/external/PackedCSparse/qd/dd_real.cc new file mode 100644 index 000000000..ff4d52231 --- /dev/null +++ b/external/PackedCSparse/qd/dd_real.cc @@ -0,0 +1,1303 @@ +/* + * src/dd_real.cc + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2007 + * + * Contains implementation of non-inlined functions of double-double + * package. Inlined functions are found in dd_inline.h (in include directory). + */ +#include +#include +#include +#include +#include +#include +#include + +#include "qd_config.h" +#include "dd_real.h" +#include "util.h" + +#include "bits.h" + +#ifndef QD_INLINE +#include "dd_inline.h" +#endif + +using std::cout; +using std::cerr; +using std::endl; +using std::ostream; +using std::istream; +using std::ios_base; +using std::string; +using std::setw; + +/* This routine is called whenever a fatal error occurs. */ +void dd_real::error(const char *msg) { + //if (msg) { cerr << "ERROR " << msg << endl; } +} + +/* Computes the square root of the double-double number dd. + NOTE: dd must be a non-negative number. */ +QD_API dd_real sqrt(const dd_real &a) { + /* Strategy: Use Karp's trick: if x is an approximation + to sqrt(a), then + + sqrt(a) = a*x + [a - (a*x)^2] * x / 2 (approx) + + The approximation is accurate to twice the accuracy of x. + Also, the multiplication (a*x) and [-]*x can be done with + only half the precision. + */ + + if (a.is_zero()) + return 0.0; + + if (a.is_negative()) { + dd_real::error("(dd_real::sqrt): Negative argument."); + return dd_real::_nan; + } + + double x = 1.0 / std::sqrt(a.x[0]); + double ax = a.x[0] * x; + return dd_real::add(ax, (a - dd_real::sqr(ax)).x[0] * (x * 0.5)); +} + +/* Computes the square root of a double in double-double precision. + NOTE: d must not be negative. */ +dd_real dd_real::sqrt(double d) { + return ::sqrt(dd_real(d)); +} + +/* Computes the n-th root of the double-double number a. + NOTE: n must be a positive integer. + NOTE: If n is even, then a must not be negative. */ +dd_real nroot(const dd_real &a, int n) { + /* Strategy: Use Newton iteration for the function + + f(x) = x^(-n) - a + + to find its root a^{-1/n}. The iteration is thus + + x' = x + x * (1 - a * x^n) / n + + which converges quadratically. We can then find + a^{1/n} by taking the reciprocal. + */ + + if (n <= 0) { + dd_real::error("(dd_real::nroot): N must be positive."); + return dd_real::_nan; + } + + if (n%2 == 0 && a.is_negative()) { + dd_real::error("(dd_real::nroot): Negative argument."); + return dd_real::_nan; + } + + if (n == 1) { + return a; + } + if (n == 2) { + return sqrt(a); + } + + if (a.is_zero()) + return 0.0; + + /* Note a^{-1/n} = exp(-log(a)/n) */ + dd_real r = abs(a); + dd_real x = std::exp(-std::log(r.x[0]) / n); + + /* Perform Newton's iteration. */ + x += x * (1.0 - r * npwr(x, n)) / static_cast(n); + if (a.x[0] < 0.0) + x = -x; + return 1.0/x; +} + +/* Computes the n-th power of a double-double number. + NOTE: 0^0 causes an error. */ +dd_real npwr(const dd_real &a, int n) { + + if (n == 0) { + if (a.is_zero()) { + dd_real::error("(dd_real::npwr): Invalid argument."); + return dd_real::_nan; + } + return 1.0; + } + + dd_real r = a; + dd_real s = 1.0; + int N = std::abs(n); + + if (N > 1) { + /* Use binary exponentiation */ + while (N > 0) { + if (N % 2 == 1) { + s *= r; + } + N /= 2; + if (N > 0) + r = sqr(r); + } + } else { + s = r; + } + + /* Compute the reciprocal if n is negative. */ + if (n < 0) + return (1.0 / s); + + return s; +} + +dd_real pow(const dd_real &a, int n) { + return npwr(a, n); +} + +dd_real pow(const dd_real &a, const dd_real &b) { + return exp(b * log(a)); +} + +static const int n_inv_fact = 15; +static const double inv_fact[n_inv_fact][2] = { + { 1.66666666666666657e-01, 9.25185853854297066e-18}, + { 4.16666666666666644e-02, 2.31296463463574266e-18}, + { 8.33333333333333322e-03, 1.15648231731787138e-19}, + { 1.38888888888888894e-03, -5.30054395437357706e-20}, + { 1.98412698412698413e-04, 1.72095582934207053e-22}, + { 2.48015873015873016e-05, 2.15119478667758816e-23}, + { 2.75573192239858925e-06, -1.85839327404647208e-22}, + { 2.75573192239858883e-07, 2.37677146222502973e-23}, + { 2.50521083854417202e-08, -1.44881407093591197e-24}, + { 2.08767569878681002e-09, -1.20734505911325997e-25}, + { 1.60590438368216133e-10, 1.25852945887520981e-26}, + { 1.14707455977297245e-11, 2.06555127528307454e-28}, + { 7.64716373181981641e-13, 7.03872877733453001e-30}, + { 4.77947733238738525e-14, 4.39920548583408126e-31}, + { 2.81145725434552060e-15, 1.65088427308614326e-31} +}; + +/* Exponential. Computes exp(x) in double-double precision. */ +dd_real exp(const dd_real &a) { + /* Strategy: We first reduce the size of x by noting that + + exp(kr + m * log(2)) = 2^m * exp(r)^k + + where m and k are integers. By choosing m appropriately + we can make |kr| <= log(2) / 2 = 0.347. Then exp(r) is + evaluated using the familiar Taylor series. Reducing the + argument substantially speeds up the convergence. */ + + const double k = 512.0; + const double inv_k = 1.0 / k; + + if (a.x[0] <= -709.0) + return 0.0; + + if (a.x[0] >= 709.0) + return dd_real::_inf; + + if (a.is_zero()) + return 1.0; + + if (a.is_one()) + return dd_real::_e; + + double m = std::floor(a.x[0] / dd_real::_log2.x[0] + 0.5); + dd_real r = mul_pwr2(a - dd_real::_log2 * m, inv_k); + dd_real s, t, p; + + p = sqr(r); + s = r + mul_pwr2(p, 0.5); + p *= r; + t = p * dd_real(inv_fact[0][0], inv_fact[0][1]); + int i = 0; + do { + s += t; + p *= r; + ++i; + t = p * dd_real(inv_fact[i][0], inv_fact[i][1]); + } while (std::abs(to_double(t)) > inv_k * dd_real::_eps && i < 5); + + s += t; + + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s += 1.0; + + return ldexp(s, static_cast(m)); +} + +/* Logarithm. Computes log(x) in double-double precision. + This is a natural logarithm (i.e., base e). */ +dd_real log(const dd_real &a) { + /* Strategy. The Taylor series for log converges much more + slowly than that of exp, due to the lack of the factorial + term in the denominator. Hence this routine instead tries + to determine the root of the function + + f(x) = exp(x) - a + + using Newton iteration. The iteration is given by + + x' = x - f(x)/f'(x) + = x - (1 - a * exp(-x)) + = x + a * exp(-x) - 1. + + Only one iteration is needed, since Newton's iteration + approximately doubles the number of digits per iteration. */ + + if (a.is_one()) { + return 0.0; + } + + if (a.x[0] <= 0.0) { + dd_real::error("(dd_real::log): Non-positive argument."); + return dd_real::_nan; + } + + dd_real x = std::log(a.x[0]); /* Initial approximation */ + + x = x + a * exp(-x) - 1.0; + return x; +} + +dd_real log10(const dd_real &a) { + return log(a) / dd_real::_log10; +} + +static const dd_real _pi16 = dd_real(1.963495408493620697e-01, + 7.654042494670957545e-18); + +/* Table of sin(k * pi/16) and cos(k * pi/16). */ +static const double sin_table [4][2] = { + {1.950903220161282758e-01, -7.991079068461731263e-18}, + {3.826834323650897818e-01, -1.005077269646158761e-17}, + {5.555702330196021776e-01, 4.709410940561676821e-17}, + {7.071067811865475727e-01, -4.833646656726456726e-17} +}; + +static const double cos_table [4][2] = { + {9.807852804032304306e-01, 1.854693999782500573e-17}, + {9.238795325112867385e-01, 1.764504708433667706e-17}, + {8.314696123025452357e-01, 1.407385698472802389e-18}, + {7.071067811865475727e-01, -4.833646656726456726e-17} +}; + +/* Computes sin(a) using Taylor series. + Assumes |a| <= pi/32. */ +static dd_real sin_taylor(const dd_real &a) { + const double thresh = 0.5 * std::abs(to_double(a)) * dd_real::_eps; + dd_real r, s, t, x; + + if (a.is_zero()) { + return 0.0; + } + + int i = 0; + x = -sqr(a); + s = a; + r = a; + do { + r *= x; + t = r * dd_real(inv_fact[i][0], inv_fact[i][1]); + s += t; + i += 2; + } while (i < n_inv_fact && std::abs(to_double(t)) > thresh); + + return s; +} + +static dd_real cos_taylor(const dd_real &a) { + const double thresh = 0.5 * dd_real::_eps; + dd_real r, s, t, x; + + if (a.is_zero()) { + return 1.0; + } + + x = -sqr(a); + r = x; + s = 1.0 + mul_pwr2(r, 0.5); + int i = 1; + do { + r *= x; + t = r * dd_real(inv_fact[i][0], inv_fact[i][1]); + s += t; + i += 2; + } while (i < n_inv_fact && std::abs(to_double(t)) > thresh); + + return s; +} + +static void sincos_taylor(const dd_real &a, + dd_real &sin_a, dd_real &cos_a) { + if (a.is_zero()) { + sin_a = 0.0; + cos_a = 1.0; + return; + } + + sin_a = sin_taylor(a); + cos_a = sqrt(1.0 - sqr(sin_a)); +} + + +dd_real sin(const dd_real &a) { + + /* Strategy. To compute sin(x), we choose integers a, b so that + + x = s + a * (pi/2) + b * (pi/16) + + and |s| <= pi/32. Using the fact that + + sin(pi/16) = 0.5 * sqrt(2 - sqrt(2 + sqrt(2))) + + we can compute sin(x) from sin(s), cos(s). This greatly + increases the convergence of the sine Taylor series. */ + + if (a.is_zero()) { + return 0.0; + } + + // approximately reduce modulo 2*pi + dd_real z = nint(a / dd_real::_2pi); + dd_real r = a - dd_real::_2pi * z; + + // approximately reduce modulo pi/2 and then modulo pi/16. + dd_real t; + double q = std::floor(r.x[0] / dd_real::_pi2.x[0] + 0.5); + t = r - dd_real::_pi2 * q; + int j = static_cast(q); + q = std::floor(t.x[0] / _pi16.x[0] + 0.5); + t -= _pi16 * q; + int k = static_cast(q); + int abs_k = std::abs(k); + + if (j < -2 || j > 2) { + dd_real::error("(dd_real::sin): Cannot reduce modulo pi/2."); + return dd_real::_nan; + } + + if (abs_k > 4) { + dd_real::error("(dd_real::sin): Cannot reduce modulo pi/16."); + return dd_real::_nan; + } + + if (k == 0) { + switch (j) { + case 0: + return sin_taylor(t); + case 1: + return cos_taylor(t); + case -1: + return -cos_taylor(t); + default: + return -sin_taylor(t); + } + } + + dd_real u(cos_table[abs_k-1][0], cos_table[abs_k-1][1]); + dd_real v(sin_table[abs_k-1][0], sin_table[abs_k-1][1]); + dd_real sin_t, cos_t; + sincos_taylor(t, sin_t, cos_t); + if (j == 0) { + if (k > 0) { + r = u * sin_t + v * cos_t; + } else { + r = u * sin_t - v * cos_t; + } + } else if (j == 1) { + if (k > 0) { + r = u * cos_t - v * sin_t; + } else { + r = u * cos_t + v * sin_t; + } + } else if (j == -1) { + if (k > 0) { + r = v * sin_t - u * cos_t; + } else if (k < 0) { + r = -u * cos_t - v * sin_t; + } + } else { + if (k > 0) { + r = -u * sin_t - v * cos_t; + } else { + r = v * cos_t - u * sin_t; + } + } + + return r; +} + +dd_real cos(const dd_real &a) { + + if (a.is_zero()) { + return 1.0; + } + + // approximately reduce modulo 2*pi + dd_real z = nint(a / dd_real::_2pi); + dd_real r = a - z * dd_real::_2pi; + + // approximately reduce modulo pi/2 and then modulo pi/16 + dd_real t; + double q = std::floor(r.x[0] / dd_real::_pi2.x[0] + 0.5); + t = r - dd_real::_pi2 * q; + int j = static_cast(q); + q = std::floor(t.x[0] / _pi16.x[0] + 0.5); + t -= _pi16 * q; + int k = static_cast(q); + int abs_k = std::abs(k); + + if (j < -2 || j > 2) { + dd_real::error("(dd_real::cos): Cannot reduce modulo pi/2."); + return dd_real::_nan; + } + + if (abs_k > 4) { + dd_real::error("(dd_real::cos): Cannot reduce modulo pi/16."); + return dd_real::_nan; + } + + if (k == 0) { + switch (j) { + case 0: + return cos_taylor(t); + case 1: + return -sin_taylor(t); + case -1: + return sin_taylor(t); + default: + return -cos_taylor(t); + } + } + + dd_real sin_t, cos_t; + sincos_taylor(t, sin_t, cos_t); + dd_real u(cos_table[abs_k-1][0], cos_table[abs_k-1][1]); + dd_real v(sin_table[abs_k-1][0], sin_table[abs_k-1][1]); + + if (j == 0) { + if (k > 0) { + r = u * cos_t - v * sin_t; + } else { + r = u * cos_t + v * sin_t; + } + } else if (j == 1) { + if (k > 0) { + r = - u * sin_t - v * cos_t; + } else { + r = v * cos_t - u * sin_t; + } + } else if (j == -1) { + if (k > 0) { + r = u * sin_t + v * cos_t; + } else { + r = u * sin_t - v * cos_t; + } + } else { + if (k > 0) { + r = v * sin_t - u * cos_t; + } else { + r = - u * cos_t - v * sin_t; + } + } + + return r; +} + +void sincos(const dd_real &a, dd_real &sin_a, dd_real &cos_a) { + + if (a.is_zero()) { + sin_a = 0.0; + cos_a = 1.0; + return; + } + + // approximately reduce modulo 2*pi + dd_real z = nint(a / dd_real::_2pi); + dd_real r = a - dd_real::_2pi * z; + + // approximately reduce module pi/2 and pi/16 + dd_real t; + double q = std::floor(r.x[0] / dd_real::_pi2.x[0] + 0.5); + t = r - dd_real::_pi2 * q; + int j = static_cast(q); + int abs_j = std::abs(j); + q = std::floor(t.x[0] / _pi16.x[0] + 0.5); + t -= _pi16 * q; + int k = static_cast(q); + int abs_k = std::abs(k); + + if (abs_j > 2) { + dd_real::error("(dd_real::sincos): Cannot reduce modulo pi/2."); + cos_a = sin_a = dd_real::_nan; + return; + } + + if (abs_k > 4) { + dd_real::error("(dd_real::sincos): Cannot reduce modulo pi/16."); + cos_a = sin_a = dd_real::_nan; + return; + } + + dd_real sin_t, cos_t; + dd_real s, c; + + sincos_taylor(t, sin_t, cos_t); + + if (abs_k == 0) { + s = sin_t; + c = cos_t; + } else { + dd_real u(cos_table[abs_k-1][0], cos_table[abs_k-1][1]); + dd_real v(sin_table[abs_k-1][0], sin_table[abs_k-1][1]); + + if (k > 0) { + s = u * sin_t + v * cos_t; + c = u * cos_t - v * sin_t; + } else { + s = u * sin_t - v * cos_t; + c = u * cos_t + v * sin_t; + } + } + + if (abs_j == 0) { + sin_a = s; + cos_a = c; + } else if (j == 1) { + sin_a = c; + cos_a = -s; + } else if (j == -1) { + sin_a = -c; + cos_a = s; + } else { + sin_a = -s; + cos_a = -c; + } + +} + +dd_real atan(const dd_real &a) { + return atan2(a, dd_real(1.0)); +} + +dd_real atan2(const dd_real &y, const dd_real &x) { + /* Strategy: Instead of using Taylor series to compute + arctan, we instead use Newton's iteration to solve + the equation + + sin(z) = y/r or cos(z) = x/r + + where r = sqrt(x^2 + y^2). + The iteration is given by + + z' = z + (y - sin(z)) / cos(z) (for equation 1) + z' = z - (x - cos(z)) / sin(z) (for equation 2) + + Here, x and y are normalized so that x^2 + y^2 = 1. + If |x| > |y|, then first iteration is used since the + denominator is larger. Otherwise, the second is used. + */ + + if (x.is_zero()) { + + if (y.is_zero()) { + /* Both x and y is zero. */ + dd_real::error("(dd_real::atan2): Both arguments zero."); + return dd_real::_nan; + } + + return (y.is_positive()) ? dd_real::_pi2 : -dd_real::_pi2; + } else if (y.is_zero()) { + return (x.is_positive()) ? dd_real(0.0) : dd_real::_pi; + } + + if (x == y) { + return (y.is_positive()) ? dd_real::_pi4 : -dd_real::_3pi4; + } + + if (x == -y) { + return (y.is_positive()) ? dd_real::_3pi4 : -dd_real::_pi4; + } + + dd_real r = sqrt(sqr(x) + sqr(y)); + dd_real xx = x / r; + dd_real yy = y / r; + + /* Compute double precision approximation to atan. */ + dd_real z = std::atan2(to_double(y), to_double(x)); + dd_real sin_z, cos_z; + + if (std::abs(xx.x[0]) > std::abs(yy.x[0])) { + /* Use Newton iteration 1. z' = z + (y - sin(z)) / cos(z) */ + sincos(z, sin_z, cos_z); + z += (yy - sin_z) / cos_z; + } else { + /* Use Newton iteration 2. z' = z - (x - cos(z)) / sin(z) */ + sincos(z, sin_z, cos_z); + z -= (xx - cos_z) / sin_z; + } + + return z; +} + +dd_real tan(const dd_real &a) { + dd_real s, c; + sincos(a, s, c); + return s/c; +} + +dd_real asin(const dd_real &a) { + dd_real abs_a = abs(a); + + if (abs_a > 1.0) { + dd_real::error("(dd_real::asin): Argument out of domain."); + return dd_real::_nan; + } + + if (abs_a.is_one()) { + return (a.is_positive()) ? dd_real::_pi2 : -dd_real::_pi2; + } + + return atan2(a, sqrt(1.0 - sqr(a))); +} + +dd_real acos(const dd_real &a) { + dd_real abs_a = abs(a); + + if (abs_a > 1.0) { + dd_real::error("(dd_real::acos): Argument out of domain."); + return dd_real::_nan; + } + + if (abs_a.is_one()) { + return (a.is_positive()) ? dd_real(0.0) : dd_real::_pi; + } + + return atan2(sqrt(1.0 - sqr(a)), a); +} + +dd_real sinh(const dd_real &a) { + if (a.is_zero()) { + return 0.0; + } + + if (abs(a) > 0.05) { + dd_real ea = exp(a); + return mul_pwr2(ea - inv(ea), 0.5); + } + + /* since a is small, using the above formula gives + a lot of cancellation. So use Taylor series. */ + dd_real s = a; + dd_real t = a; + dd_real r = sqr(t); + double m = 1.0; + double thresh = std::abs((to_double(a)) * dd_real::_eps); + + do { + m += 2.0; + t *= r; + t /= (m-1) * m; + + s += t; + } while (abs(t) > thresh); + + return s; + +} + +dd_real cosh(const dd_real &a) { + if (a.is_zero()) { + return 1.0; + } + + dd_real ea = exp(a); + return mul_pwr2(ea + inv(ea), 0.5); +} + +dd_real tanh(const dd_real &a) { + if (a.is_zero()) { + return 0.0; + } + + if (std::abs(to_double(a)) > 0.05) { + dd_real ea = exp(a); + dd_real inv_ea = inv(ea); + return (ea - inv_ea) / (ea + inv_ea); + } else { + dd_real s, c; + s = sinh(a); + c = sqrt(1.0 + sqr(s)); + return s / c; + } +} + +void sincosh(const dd_real &a, dd_real &s, dd_real &c) { + if (std::abs(to_double(a)) <= 0.05) { + s = sinh(a); + c = sqrt(1.0 + sqr(s)); + } else { + dd_real ea = exp(a); + dd_real inv_ea = inv(ea); + s = mul_pwr2(ea - inv_ea, 0.5); + c = mul_pwr2(ea + inv_ea, 0.5); + } +} + +dd_real asinh(const dd_real &a) { + return log(a + sqrt(sqr(a) + 1.0)); +} + +dd_real acosh(const dd_real &a) { + if (a < 1.0) { + dd_real::error("(dd_real::acosh): Argument out of domain."); + return dd_real::_nan; + } + + return log(a + sqrt(sqr(a) - 1.0)); +} + +dd_real atanh(const dd_real &a) { + if (abs(a) >= 1.0) { + dd_real::error("(dd_real::atanh): Argument out of domain."); + return dd_real::_nan; + } + + return mul_pwr2(log((1.0 + a) / (1.0 - a)), 0.5); +} + +QD_API dd_real fmod(const dd_real &a, const dd_real &b) { + dd_real n = aint(a / b); + return (a - b * n); +} + +QD_API dd_real ddrand() { + static const double m_const = 4.6566128730773926e-10; /* = 2^{-31} */ + double m = m_const; + dd_real r = 0.0; + double d; + + /* Strategy: Generate 31 bits at a time, using lrand48 + random number generator. Shift the bits, and reapeat + 4 times. */ + + for (int i = 0; i < 4; i++, m *= m_const) { +// d = lrand48() * m; + d = std::rand() * m; + r += d; + } + + return r; +} + +/* polyeval(c, n, x) + Evaluates the given n-th degree polynomial at x. + The polynomial is given by the array of (n+1) coefficients. */ +dd_real polyeval(const dd_real *c, int n, const dd_real &x) { + /* Just use Horner's method of polynomial evaluation. */ + dd_real r = c[n]; + + for (int i = n-1; i >= 0; i--) { + r *= x; + r += c[i]; + } + + return r; +} + +/* polyroot(c, n, x0) + Given an n-th degree polynomial, finds a root close to + the given guess x0. Note that this uses simple Newton + iteration scheme, and does not work for multiple roots. */ +QD_API dd_real polyroot(const dd_real *c, int n, + const dd_real &x0, int max_iter, double thresh) { + dd_real x = x0; + dd_real f; + dd_real *d = new dd_real[n]; + bool conv = false; + int i; + double max_c = std::abs(to_double(c[0])); + double v; + + if (thresh == 0.0) thresh = dd_real::_eps; + + /* Compute the coefficients of the derivatives. */ + for (i = 1; i <= n; i++) { + v = std::abs(to_double(c[i])); + if (v > max_c) max_c = v; + d[i-1] = c[i] * static_cast(i); + } + thresh *= max_c; + + /* Newton iteration. */ + for (i = 0; i < max_iter; i++) { + f = polyeval(c, n, x); + + if (abs(f) < thresh) { + conv = true; + break; + } + x -= (f / polyeval(d, n-1, x)); + } + delete [] d; + + if (!conv) { + dd_real::error("(dd_real::polyroot): Failed to converge."); + return dd_real::_nan; + } + + return x; +} + + +/* Constructor. Reads a double-double number from the string s + and constructs a double-double number. */ +dd_real::dd_real(const char *s) { + if (dd_real::read(s, *this)) { + dd_real::error("(dd_real::dd_real): INPUT ERROR."); + *this = dd_real::_nan; + } +} + +dd_real &dd_real::operator=(const char *s) { + if (dd_real::read(s, *this)) { + dd_real::error("(dd_real::operator=): INPUT ERROR."); + *this = dd_real::_nan; + } + return *this; +} + +/* Outputs the double-double number dd. */ +ostream &operator<<(ostream &os, const dd_real &dd) { + bool showpos = (os.flags() & ios_base::showpos) != 0; + bool uppercase = (os.flags() & ios_base::uppercase) != 0; + return os << dd.to_string((int)os.precision(), (int)os.width(), os.flags(), + showpos, uppercase, os.fill()); +} + +/* Reads in the double-double number a. */ +istream &operator>>(istream &s, dd_real &a) { + char str[255]; + s >> str; + a = dd_real(str); + return s; +} + +void dd_real::to_digits(char *s, int &expn, int precision) const { + int D = precision + 1; /* number of digits to compute */ + + dd_real r = abs(*this); + int e; /* exponent */ + int i, d; + + if (x[0] == 0.0) { + /* this == 0.0 */ + expn = 0; + for (i = 0; i < precision; i++) s[i] = '0'; + return; + } + + /* First determine the (approximate) exponent. */ + e = to_int(std::floor(std::log10(std::abs(x[0])))); + + if (e < -300) { + r *= dd_real(10.0) ^ 300; + r /= dd_real(10.0) ^ (e + 300); + } else if (e > 300) { + r = ldexp(r, -53); + r /= dd_real(10.0) ^ e; + r = ldexp(r, 53); + } else { + r /= dd_real(10.0) ^ e; + } + + /* Fix exponent if we are off by one */ + if (r >= 10.0) { + r /= 10.0; + e++; + } else if (r < 1.0) { + r *= 10.0; + e--; + } + + if (r >= 10.0 || r < 1.0) { + dd_real::error("(dd_real::to_digits): can't compute exponent."); + return; + } + + /* Extract the digits */ + for (i = 0; i < D; i++) { + d = static_cast(r.x[0]); + r -= d; + r *= 10.0; + + s[i] = static_cast(d + '0'); + } + + /* Fix out of range digits. */ + for (i = D-1; i > 0; i--) { + if (s[i] < '0') { + s[i-1]--; + s[i] += 10; + } else if (s[i] > '9') { + s[i-1]++; + s[i] -= 10; + } + } + + if (s[0] <= '0') { + dd_real::error("(dd_real::to_digits): non-positive leading digit."); + return; + } + + /* Round, handle carry */ + if (s[D-1] >= '5') { + s[D-2]++; + + i = D-2; + while (i > 0 && s[i] > '9') { + s[i] -= 10; + s[--i]++; + } + } + + /* If first digit is 10, shift everything. */ + if (s[0] > '9') { + e++; + for (i = precision; i >= 2; i--) s[i] = s[i-1]; + s[0] = '1'; + s[1] = '0'; + } + + s[precision] = 0; + expn = e; +} + +/* Writes the double-double number into the character array s of length len. + The integer d specifies how many significant digits to write. + The string s must be able to hold at least (d+8) characters. + showpos indicates whether to use the + sign, and uppercase indicates + whether the E or e is to be used for the exponent. */ +void dd_real::write(char *s, int len, int precision, + bool showpos, bool uppercase) const { + string str = to_string(precision, 0, ios_base::scientific, showpos, uppercase); + std::strncpy(s, str.c_str(), len-1); + s[len-1] = 0; +} + + +void round_string(char *s, int precision, int *offset){ + /* + Input string must be all digits or errors will occur. + */ + + int i; + int D = precision ; + + /* Round, handle carry */ + if (D>0 && s[D] >= '5') { + s[D-1]++; + + i = D-1; + while (i > 0 && s[i] > '9') { + s[i] -= 10; + s[--i]++; + } + } + + /* If first digit is 10, shift everything. */ + if (s[0] > '9') { + // e++; // don't modify exponent here + for (i = precision; i >= 1; i--) s[i+1] = s[i]; + s[0] = '1'; + s[1] = '0'; + + (*offset)++ ; // now offset needs to be increased by one + precision++ ; + } + + s[precision] = 0; // add terminator for array +} + +string dd_real::to_string(int precision, int width, ios_base::fmtflags fmt, + bool showpos, bool uppercase, char fill) const { + string s; + bool fixed = (fmt & ios_base::fixed) != 0; + bool sgn = true; + int i, e = 0; + + if (isnan()) { + s = uppercase ? "NAN" : "nan"; + sgn = false; + } else { + if (*this < 0.0) + s += '-'; + else if (showpos) + s += '+'; + else + sgn = false; + + if (isinf()) { + s += uppercase ? "INF" : "inf"; + } else if (*this == 0.0) { + /* Zero case */ + s += '0'; + if (precision > 0) { + s += '.'; + s.append(precision, '0'); + } + } else { + /* Non-zero case */ + int off = (fixed ? (1 + to_int(floor(log10(abs(*this))))) : 1); + int d = precision + off; + + int d_with_extra = d; + if(fixed) + d_with_extra = std::max(60, d); // longer than the max accuracy for DD + + // highly special case - fixed mode, precision is zero, abs(*this) < 1.0 + // without this trap a number like 0.9 printed fixed with 0 precision prints as 0 + // should be rounded to 1. + if(fixed && (precision == 0) && (abs(*this) < 1.0)){ + if(abs(*this) >= 0.5) + s += '1'; + else + s += '0'; + + return s; + } + + // handle near zero to working precision (but not exactly zero) + if (fixed && d <= 0) { + s += '0'; + if (precision > 0) { + s += '.'; + s.append(precision, '0'); + } + } else { // default + + char *t; // = new char[d+1]; + int j; + + if(fixed){ + t = new char[d_with_extra+1]; + to_digits(t, e, d_with_extra); + } + else{ + t = new char[d+1]; + to_digits(t, e, d); + } + + off = e + 1; + + if (fixed) { + // fix the string if it's been computed incorrectly + // round here in the decimal string if required + round_string(t, d, &off); + + if (off > 0) { + for (i = 0; i < off; i++) s += t[i]; + if (precision > 0) { + s += '.'; + for (j = 0; j < precision; j++, i++) s += t[i]; + } + } else { + s += "0."; + if (off < 0) s.append(-off, '0'); + for (i = 0; i < d; i++) s += t[i]; + } + } else { + s += t[0]; + if (precision > 0) s += '.'; + + for (i = 1; i <= precision; i++) + s += t[i]; + + } + delete [] t; + } + } + + // trap for improper offset with large values + // without this trap, output of values of the for 10^j - 1 fail for j > 28 + // and are output with the point in the wrong place, leading to a dramatically off value + if(fixed && (precision > 0)){ + // make sure that the value isn't dramatically larger + double from_string = atof(s.c_str()); + + // if this ratio is large, then we've got problems + if( fabs( from_string / this->x[0] ) > 3.0 ){ + + // loop on the string, find the point, move it up one + // don't act on the first character + for(i=1; i < (int)s.length(); i++){ + if(s[i] == '.'){ + s[i] = s[i-1] ; + s[i-1] = '.' ; + break; + } + } + + from_string = atof(s.c_str()); + // if this ratio is large, then the string has not been fixed + if( fabs( from_string / this->x[0] ) > 3.0 ){ + dd_real::error("Re-rounding unsuccessful in large number fixed point trap.") ; + } + } + } + + + if (!fixed && !isinf()) { + /* Fill in exponent part */ + s += uppercase ? 'E' : 'e'; + append_expn(s, e); + } + } + + /* Fill in the blanks */ + int len = s.length(); + if (len < width) { + int delta = width - len; + if (fmt & ios_base::internal) { + if (sgn) + s.insert(static_cast(1), delta, fill); + else + s.insert(static_cast(0), delta, fill); + } else if (fmt & ios_base::left) { + s.append(delta, fill); + } else { + s.insert(static_cast(0), delta, fill); + } + } + + return s; +} + +/* Reads in a double-double number from the string s. */ +int dd_real::read(const char *s, dd_real &a) { + const char *p = s; + char ch; + int sign = 0; + int point = -1; + int nd = 0; + int e = 0; + bool done = false; + dd_real r = 0.0; + int nread; + + /* Skip any leading spaces */ + while (*p == ' ') + p++; + + while (!done && (ch = *p) != '\0') { + if (ch >= '0' && ch <= '9') { + int d = ch - '0'; + r *= 10.0; + r += static_cast(d); + nd++; + } else { + + switch (ch) { + + case '.': + if (point >= 0) + return -1; + point = nd; + break; + + case '-': + case '+': + if (sign != 0 || nd > 0) + return -1; + sign = (ch == '-') ? -1 : 1; + break; + + case 'E': + case 'e': + nread = std::sscanf(p+1, "%d", &e); + done = true; + if (nread != 1) + return -1; + break; + + default: + return -1; + } + } + + p++; + } + + if (point >= 0) { + e -= (nd - point); + } + + if (e != 0) { + r *= (dd_real(10.0) ^ e); + } + + a = (sign == -1) ? -r : r; + return 0; +} + +/* Debugging routines */ +void dd_real::dump(const string &name, std::ostream &os) const { + std::ios_base::fmtflags old_flags = os.flags(); + std::streamsize old_prec = os.precision(19); + os << std::scientific; + + if (name.length() > 0) os << name << " = "; + os << "[ " << setw(27) << x[0] << ", " << setw(27) << x[1] << " ]" << endl; + + os.precision(old_prec); + os.flags(old_flags); +} + +void dd_real::dump_bits(const string &name, std::ostream &os) const { + string::size_type len = name.length(); + if (len > 0) { + os << name << " = "; + len +=3; + } + os << "[ "; + len += 2; + print_double_info(os, x[0]); + os << endl; + for (string::size_type i = 0; i < len; i++) os << ' '; + print_double_info(os, x[1]); + os << " ]" << endl; +} + +dd_real dd_real::debug_rand() { + + if (std::rand() % 2 == 0) + return ddrand(); + + int expn = 0; + dd_real a = 0.0; + double d; + for (int i = 0; i < 2; i++) { + d = std::ldexp(static_cast(std::rand()) / RAND_MAX, -expn); + a += d; + expn = expn + 54 + std::rand() % 200; + } + return a; +} diff --git a/external/PackedCSparse/dd_real.h b/external/PackedCSparse/qd/dd_real.h similarity index 92% rename from external/PackedCSparse/dd_real.h rename to external/PackedCSparse/qd/dd_real.h index 5fe3491e5..e16438aa0 100644 --- a/external/PackedCSparse/dd_real.h +++ b/external/PackedCSparse/qd/dd_real.h @@ -1,12 +1,3 @@ -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2018 Vissarion Fisikopoulos -// Copyright (c) 2018 Apostolos Chalkis -// Copyright (c) 2022 Ioannis Iakovidis - -// This file is converted from QD library -//(https://www.davidhbailey.com/dhbsoftware) by Ioannis Iakovidis - /* * include/dd_real.h * @@ -18,14 +9,14 @@ * * Double-double precision (>= 106-bit significand) floating point * arithmetic package based on David Bailey's Fortran-90 double-double - * package, with some changes. See + * package, with some changes. See * * http://www.nersc.gov/~dhbailey/mpdist/mpdist.html - * + * * for the original Fortran-90 version. * * Overall structure is similar to that of Keith Brigg's C++ double-double - * package. See + * package. See * * http://www-epidem.plansci.cam.ac.uk/~kbriggs/doubledouble.html * @@ -42,8 +33,8 @@ #include #include #include -#include "qd/qd_config.h" -#include "qd/fpu.h" +#include "qd_config.h" +#include "fpu.h" // Some compilers define isnan, isfinite, and isinf as macros, even for // C++ codes, which cause havoc when overloading these functions. We undef @@ -70,11 +61,7 @@ struct QD_API dd_real { double x[2]; - inline operator bool() const // new - { - return (x[0] != 0.0); - } dd_real(double hi, double lo) { x[0] = hi; x[1] = lo; } dd_real() {x[0] = 0.0; x[1] = 0.0; } dd_real(double h) { x[0] = h; x[1] = 0.0; } @@ -136,7 +123,7 @@ struct QD_API dd_real { static dd_real div(double a, double b); static dd_real sloppy_div(const dd_real &a, const dd_real &b); static dd_real accurate_div(const dd_real &a, const dd_real &b); - + dd_real &operator/=(double a); dd_real &operator/=(const dd_real &a); @@ -147,26 +134,28 @@ struct QD_API dd_real { static dd_real sqr(double d); static dd_real sqrt(double a); - + bool is_zero() const; bool is_one() const; bool is_positive() const; bool is_negative() const; + explicit operator bool() const; // new + explicit operator double() const; // new static dd_real rand(void); void to_digits(char *s, int &expn, int precision = _ndigits) const; - void write(char *s, int len, int precision = _ndigits, + void write(char *s, int len, int precision = _ndigits, bool showpos = false, bool uppercase = false) const; - std::string to_string(int precision = _ndigits, int width = 0, - std::ios_base::fmtflags fmt = static_cast(0), + std::string to_string(int precision = _ndigits, int width = 0, + std::ios_base::fmtflags fmt = static_cast(0), bool showpos = false, bool uppercase = false, char fill = ' ') const; int read(const char *s, dd_real &a); /* Debugging Methods */ void dump(const std::string &name, std::ostream &os = std::cerr) const; - void dump_bits(const std::string &name, + void dump_bits(const std::string &name, std::ostream &os = std::cerr) const; static dd_real debug_rand(); @@ -190,7 +179,7 @@ QD_API dd_real ddrand(void); QD_API dd_real sqrt(const dd_real &a); QD_API dd_real polyeval(const dd_real *c, int n, const dd_real &x); -QD_API dd_real polyroot(const dd_real *c, int n, +QD_API dd_real polyroot(const dd_real *c, int n, const dd_real &x0, int max_iter = 32, double thresh = 0.0); QD_API inline bool isnan(const dd_real &a) { return a.isnan(); } @@ -282,7 +271,7 @@ QD_API dd_real atan2(const dd_real &y, const dd_real &x); QD_API dd_real sinh(const dd_real &a); QD_API dd_real cosh(const dd_real &a); QD_API dd_real tanh(const dd_real &a); -QD_API void sincosh(const dd_real &a, +QD_API void sincosh(const dd_real &a, dd_real &sinh_a, dd_real &cosh_a); QD_API dd_real asinh(const dd_real &a); @@ -297,7 +286,8 @@ QD_API dd_real fmod(const dd_real &a, const dd_real &b); QD_API std::ostream& operator<<(std::ostream &s, const dd_real &a); QD_API std::istream& operator>>(std::istream &s, dd_real &a); #ifdef QD_INLINE -#include "qd/dd_inline.h" +#include "dd_inline.h" #endif #endif /* _QD_DD_REAL_H */ + diff --git a/external/PackedCSparse/qd/fpu.cc b/external/PackedCSparse/qd/fpu.cc new file mode 100644 index 000000000..96ddc488f --- /dev/null +++ b/external/PackedCSparse/qd/fpu.cc @@ -0,0 +1,124 @@ +/* + * src/fpu.cc + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2001 + * + * Contains functions to set and restore the round-to-double flag in the + * control word of a x86 FPU. + */ + +#include "qd_config.h" +#include "fpu.h" + +#ifdef X86 +#ifdef _WIN32 +#include +#else + +#ifdef HAVE_FPU_CONTROL_H +#include +#endif + +#ifndef _FPU_GETCW +#define _FPU_GETCW(x) asm volatile ("fnstcw %0":"=m" (x)); +#endif + +#ifndef _FPU_SETCW +#define _FPU_SETCW(x) asm volatile ("fldcw %0": :"m" (x)); +#endif + +#ifndef _FPU_EXTENDED +#define _FPU_EXTENDED 0x0300 +#endif + +#ifndef _FPU_DOUBLE +#define _FPU_DOUBLE 0x0200 +#endif + +#endif +#endif /* X86 */ + +extern "C" { + +void fpu_fix_start(unsigned int *old_cw) { +#ifdef X86 +#ifdef _WIN32 +#ifdef __BORLANDC__ + /* Win 32 Borland C */ + unsigned short cw = _control87(0, 0); + _control87(0x0200, 0x0300); + if (old_cw) { + *old_cw = cw; + } +#else + /* Win 32 MSVC */ + unsigned int cw = _control87(0, 0); + _control87(0x00010000, 0x00030000); + if (old_cw) { + *old_cw = cw; + } +#endif +#else + /* Linux */ + volatile unsigned short cw, new_cw; + _FPU_GETCW(cw); + + new_cw = (cw & ~_FPU_EXTENDED) | _FPU_DOUBLE; + _FPU_SETCW(new_cw); + + if (old_cw) { + *old_cw = cw; + } +#endif +#endif +} + +void fpu_fix_end(unsigned int *old_cw) { +#ifdef X86 +#ifdef _WIN32 + +#ifdef __BORLANDC__ + /* Win 32 Borland C */ + if (old_cw) { + unsigned short cw = (unsigned short) *old_cw; + _control87(cw, 0xFFFF); + } +#else + /* Win 32 MSVC */ + if (old_cw) { + _control87(*old_cw, 0xFFFFFFFF); + } +#endif + +#else + /* Linux */ + if (old_cw) { + int cw; + cw = *old_cw; + _FPU_SETCW(cw); + } +#endif +#endif +} + +#ifdef HAVE_FORTRAN + +#define f_fpu_fix_start FC_FUNC_(f_fpu_fix_start, F_FPU_FIX_START) +#define f_fpu_fix_end FC_FUNC_(f_fpu_fix_end, F_FPU_FIX_END) + +void f_fpu_fix_start(unsigned int *old_cw) { + fpu_fix_start(old_cw); +} + +void f_fpu_fix_end(unsigned int *old_cw) { + fpu_fix_end(old_cw); +} + +#endif + +} + diff --git a/external/PackedCSparse/qd/fpu.h b/external/PackedCSparse/qd/fpu.h new file mode 100644 index 000000000..35eab18cf --- /dev/null +++ b/external/PackedCSparse/qd/fpu.h @@ -0,0 +1,39 @@ +/* + * include/fpu.h + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2001 + * + * Contains functions to set and restore the round-to-double flag in the + * control word of a x86 FPU. The algorithms in the double-double and + * quad-double package does not function with the extended mode found in + * these FPU. + */ +#ifndef _QD_FPU_H +#define _QD_FPU_H + +#include "qd_config.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/* + * Set the round-to-double flag, and save the old control word in old_cw. + * If old_cw is NULL, the old control word is not saved. + */ +QD_API void fpu_fix_start(unsigned int *old_cw); + +/* + * Restore the control word. + */ +QD_API void fpu_fix_end(unsigned int *old_cw); + +#ifdef __cplusplus +} +#endif + +#endif /* _QD_FPU_H */ diff --git a/external/PackedCSparse/qd/inline.h b/external/PackedCSparse/qd/inline.h new file mode 100644 index 000000000..52425545b --- /dev/null +++ b/external/PackedCSparse/qd/inline.h @@ -0,0 +1,143 @@ +/* + * include/inline.h + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2001 + * + * This file contains the basic functions used both by double-double + * and quad-double package. These are declared as inline functions as + * they are the smallest building blocks of the double-double and + * quad-double arithmetic. + */ +#ifndef _QD_INLINE_H +#define _QD_INLINE_H + +#define _QD_SPLITTER 134217729.0 // = 2^27 + 1 +#define _QD_SPLIT_THRESH 6.69692879491417e+299 // = 2^996 + +#ifdef QD_VACPP_BUILTINS_H +/* For VisualAge C++ __fmadd */ +#include +#endif + +#include +#include + +namespace qd { + +static const double _d_nan = std::numeric_limits::quiet_NaN(); +static const double _d_inf = std::numeric_limits::infinity(); + +/*********** Basic Functions ************/ +/* Computes fl(a+b) and err(a+b). Assumes |a| >= |b|. */ +inline double quick_two_sum(double a, double b, double &err) { + double s = a + b; + err = b - (s - a); + return s; +} + +/* Computes fl(a-b) and err(a-b). Assumes |a| >= |b| */ +inline double quick_two_diff(double a, double b, double &err) { + double s = a - b; + err = (a - s) - b; + return s; +} + +/* Computes fl(a+b) and err(a+b). */ +inline double two_sum(double a, double b, double &err) { + double s = a + b; + double bb = s - a; + err = (a - (s - bb)) + (b - bb); + return s; +} + +/* Computes fl(a-b) and err(a-b). */ +inline double two_diff(double a, double b, double &err) { + double s = a - b; + double bb = s - a; + err = (a - (s - bb)) - (b + bb); + return s; +} + +#ifndef QD_FMS +/* Computes high word and lo word of a */ +inline void split(double a, double &hi, double &lo) { + double temp; + if (a > _QD_SPLIT_THRESH || a < -_QD_SPLIT_THRESH) { + a *= 3.7252902984619140625e-09; // 2^-28 + temp = _QD_SPLITTER * a; + hi = temp - (temp - a); + lo = a - hi; + hi *= 268435456.0; // 2^28 + lo *= 268435456.0; // 2^28 + } else { + temp = _QD_SPLITTER * a; + hi = temp - (temp - a); + lo = a - hi; + } +} +#endif + +/* Computes fl(a*b) and err(a*b). */ +inline double two_prod(double a, double b, double &err) { +#ifdef QD_FMS + double p = a * b; + err = QD_FMS(a, b, p); + return p; +#else + double a_hi, a_lo, b_hi, b_lo; + double p = a * b; + split(a, a_hi, a_lo); + split(b, b_hi, b_lo); + err = ((a_hi * b_hi - p) + a_hi * b_lo + a_lo * b_hi) + a_lo * b_lo; + return p; +#endif +} + +/* Computes fl(a*a) and err(a*a). Faster than the above method. */ +inline double two_sqr(double a, double &err) { +#ifdef QD_FMS + double p = a * a; + err = QD_FMS(a, a, p); + return p; +#else + double hi, lo; + double q = a * a; + split(a, hi, lo); + err = ((hi * hi - q) + 2.0 * hi * lo) + lo * lo; + return q; +#endif +} + +/* Computes the nearest integer to d. */ +inline double nint(double d) { + if (d == std::floor(d)) + return d; + return std::floor(d + 0.5); +} + +/* Computes the truncated integer. */ +inline double aint(double d) { + return (d >= 0.0) ? std::floor(d) : std::ceil(d); +} + +/* These are provided to give consistent + interface for double with double-double and quad-double. */ +inline void sincosh(double t, double &sinh_t, double &cosh_t) { + sinh_t = std::sinh(t); + cosh_t = std::cosh(t); +} + +inline double sqr(double t) { + return t * t; +} + +inline double to_double(double a) { return a; } +inline int to_int(double a) { return static_cast(a); } + +} + +#endif /* _QD_INLINE_H */ diff --git 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z`UpgrwE`LosCJ3^A(@ASPNZQdc8q?omndy7=)yC;0t6zOA0kAh^8n=1xrSuef|Q;M zKiJiN{F|7QQ1BTX5hnjMekzqOT8qi#GXR8LkB~&`j3I1H&j9@BItj}q+xtN0L+3sC zkI=OQWbkx6G=8-2q3j}_&QqAo)YEt~Px%kY0^M6^{Aj#EDS^OIa{+{O-2gjOiH;GH zCAt^Y_%ZQ-j*G??kR4A(l}Frr^>0_ zv~#BXKp?_FRpatG(+IySz`?4)<-;4{R&IP+wxndLWEiNQNz^Pg6Xj%q(Y%$HCE03K hWy>Frn5eT@bm53S6p8;=0amqEP&at+u)G}Ie*j?K +#include + +/* include/qd/qd_config.h. Generated from qd_config.h.in by configure. */ +#ifndef _QD_QD_CONFIG_H +#define _QD_QD_CONFIG_H 1 + +#ifndef QD_API +#define QD_API /**/ +#endif + +/* Set to 1 if using VisualAge C++ compiler for __fmadd builtin. */ +#ifndef QD_VACPP_BUILTINS_H +/* #undef QD_VACPP_BUILTINS_H */ +#endif + +/* If fused multiply-add is available, define to correct macro for + using it. It is invoked as QD_FMA(a, b, c) to compute fl(a * b + c). + If correctly rounded multiply-add is not available (or if unsure), + keep it undefined.*/ +#ifndef QD_FMA +/* #undef QD_FMA */ +#endif + +/* If fused multiply-subtract is available, define to correct macro for + using it. It is invoked as QD_FMS(a, b, c) to compute fl(a * b - c). + If correctly rounded multiply-add is not available (or if unsure), + keep it undefined.*/ +#ifndef QD_FMS +#define QD_FMS(a, b, c) std::fma(a,b,-c) +/* #undef QD_FMS */ +#endif + +/* Set the following to 1 to define commonly used function + to be inlined. This should be set to 1 unless the compiler + does not support the "inline" keyword, or if building for + debugging purposes. */ +#ifndef QD_INLINE +#define QD_INLINE 1 +#endif + +/* Set the following to 1 to use ANSI C++ standard header files + such as cmath, iostream, etc. If set to zero, it will try to + include math.h, iostream.h, etc, instead. */ +#ifndef QD_HAVE_STD +#define QD_HAVE_STD 1 +#endif + +/* Set the following to 1 to make the addition and subtraction + to satisfy the IEEE-style error bound + + fl(a + b) = (1 + d) * (a + b) + + where |d| <= eps. If set to 0, the addition and subtraction + will satisfy the weaker Cray-style error bound + + fl(a + b) = (1 + d1) * a + (1 + d2) * b + + where |d1| <= eps and |d2| eps. */ +#ifndef QD_IEEE_ADD +/* #undef QD_IEEE_ADD */ +#endif + +/* Set the following to 1 to use slightly inaccurate but faster + version of multiplication. */ +#ifndef QD_SLOPPY_MUL +#define QD_SLOPPY_MUL 1 +#endif + +/* Set the following to 1 to use slightly inaccurate but faster + version of division. */ +#ifndef QD_SLOPPY_DIV +#define QD_SLOPPY_DIV 1 +#endif + +/* Define this macro to be the isfinite(x) function. */ +#ifndef QD_ISFINITE +#define QD_ISFINITE(x) std::isfinite(x) +#endif + +/* Define this macro to be the isinf(x) function. */ +#ifndef QD_ISINF +#define QD_ISINF(x) std::isinf(x) +#endif + +/* Define this macro to be the isnan(x) function. */ +#ifndef QD_ISNAN +#define QD_ISNAN(x) std::isnan(x) +#endif + + +#endif /* _QD_QD_CONFIG_H */ diff --git a/external/PackedCSparse/qd/qd_const.cc b/external/PackedCSparse/qd/qd_const.cc new file mode 100644 index 000000000..6f4e01d2a --- /dev/null +++ b/external/PackedCSparse/qd/qd_const.cc @@ -0,0 +1,62 @@ +/* + * src/qd_const.cc + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2001 + * + * Defines constants used in quad-double package. + */ +#include "qd_config.h" +#include "qd_real.h" + +/* Some useful constants. */ +const qd_real qd_real::_2pi = qd_real(6.283185307179586232e+00, + 2.449293598294706414e-16, + -5.989539619436679332e-33, + 2.224908441726730563e-49); +const qd_real qd_real::_pi = qd_real(3.141592653589793116e+00, + 1.224646799147353207e-16, + -2.994769809718339666e-33, + 1.112454220863365282e-49); +const qd_real qd_real::_pi2 = qd_real(1.570796326794896558e+00, + 6.123233995736766036e-17, + -1.497384904859169833e-33, + 5.562271104316826408e-50); +const qd_real qd_real::_pi4 = qd_real(7.853981633974482790e-01, + 3.061616997868383018e-17, + -7.486924524295849165e-34, + 2.781135552158413204e-50); +const qd_real qd_real::_3pi4 = qd_real(2.356194490192344837e+00, + 9.1848509936051484375e-17, + 3.9168984647504003225e-33, + -2.5867981632704860386e-49); +const qd_real qd_real::_e = qd_real(2.718281828459045091e+00, + 1.445646891729250158e-16, + -2.127717108038176765e-33, + 1.515630159841218954e-49); +const qd_real qd_real::_log2 = qd_real(6.931471805599452862e-01, + 2.319046813846299558e-17, + 5.707708438416212066e-34, + -3.582432210601811423e-50); +const qd_real qd_real::_log10 = qd_real(2.302585092994045901e+00, + -2.170756223382249351e-16, + -9.984262454465776570e-33, + -4.023357454450206379e-49); +const qd_real qd_real::_nan = qd_real(qd::_d_nan, qd::_d_nan, + qd::_d_nan, qd::_d_nan); +const qd_real qd_real::_inf = qd_real(qd::_d_inf, qd::_d_inf, + qd::_d_inf, qd::_d_inf); + +const double qd_real::_eps = 1.21543267145725e-63; // = 2^-209 +const double qd_real::_min_normalized = 1.6259745436952323e-260; // = 2^(-1022 + 3*53) +const qd_real qd_real::_max = qd_real( + 1.79769313486231570815e+308, 9.97920154767359795037e+291, + 5.53956966280111259858e+275, 3.07507889307840487279e+259); +const qd_real qd_real::_safe_max = qd_real( + 1.7976931080746007281e+308, 9.97920154767359795037e+291, + 5.53956966280111259858e+275, 3.07507889307840487279e+259); +const int qd_real::_ndigits = 62; + diff --git a/external/PackedCSparse/qd/qd_inline.h b/external/PackedCSparse/qd/qd_inline.h new file mode 100644 index 000000000..89ba275b5 --- /dev/null +++ b/external/PackedCSparse/qd/qd_inline.h @@ -0,0 +1,1047 @@ +/* + * include/qd_inline.h + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2001 + * + * Contains small functions (suitable for inlining) in the quad-double + * arithmetic package. + */ +#ifndef _QD_QD_INLINE_H +#define _QD_QD_INLINE_H + +#include +#include "inline.h" + +#ifndef QD_INLINE +#define inline +#endif + +/********** Constructors **********/ +inline qd_real::qd_real(double x0, double x1, double x2, double x3) { + x[0] = x0; + x[1] = x1; + x[2] = x2; + x[3] = x3; +} + +inline qd_real::qd_real(const double *xx) { + x[0] = xx[0]; + x[1] = xx[1]; + x[2] = xx[2]; + x[3] = xx[3]; +} + +inline qd_real::qd_real(double x0) { + x[0] = x0; + x[1] = x[2] = x[3] = 0.0; +} + +inline qd_real::qd_real() { + x[0] = 0.0; + x[1] = 0.0; + x[2] = 0.0; + x[3] = 0.0; +} + +inline qd_real::qd_real(const dd_real &a) { + x[0] = a._hi(); + x[1] = a._lo(); + x[2] = x[3] = 0.0; +} + +inline qd_real::qd_real(int i) { + x[0] = static_cast(i); + x[1] = x[2] = x[3] = 0.0; +} + +/********** Accessors **********/ +inline double qd_real::operator[](int i) const { + return x[i]; +} + +inline double &qd_real::operator[](int i) { + return x[i]; +} + +inline bool qd_real::isnan() const { + return QD_ISNAN(x[0]) || QD_ISNAN(x[1]) || QD_ISNAN(x[2]) || QD_ISNAN(x[3]); +} + +/********** Renormalization **********/ +namespace qd { +inline void quick_renorm(double &c0, double &c1, + double &c2, double &c3, double &c4) { + double t0, t1, t2, t3; + double s; + s = qd::quick_two_sum(c3, c4, t3); + s = qd::quick_two_sum(c2, s , t2); + s = qd::quick_two_sum(c1, s , t1); + c0 = qd::quick_two_sum(c0, s , t0); + + s = qd::quick_two_sum(t2, t3, t2); + s = qd::quick_two_sum(t1, s , t1); + c1 = qd::quick_two_sum(t0, s , t0); + + s = qd::quick_two_sum(t1, t2, t1); + c2 = qd::quick_two_sum(t0, s , t0); + + c3 = t0 + t1; +} + +inline void renorm(double &c0, double &c1, + double &c2, double &c3) { + double s0, s1, s2 = 0.0, s3 = 0.0; + + if (QD_ISINF(c0)) return; + + s0 = qd::quick_two_sum(c2, c3, c3); + s0 = qd::quick_two_sum(c1, s0, c2); + c0 = qd::quick_two_sum(c0, s0, c1); + + s0 = c0; + s1 = c1; + if (s1 != 0.0) { + s1 = qd::quick_two_sum(s1, c2, s2); + if (s2 != 0.0) + s2 = qd::quick_two_sum(s2, c3, s3); + else + s1 = qd::quick_two_sum(s1, c3, s2); + } else { + s0 = qd::quick_two_sum(s0, c2, s1); + if (s1 != 0.0) + s1 = qd::quick_two_sum(s1, c3, s2); + else + s0 = qd::quick_two_sum(s0, c3, s1); + } + + c0 = s0; + c1 = s1; + c2 = s2; + c3 = s3; +} + +inline void renorm(double &c0, double &c1, + double &c2, double &c3, double &c4) { + double s0, s1, s2 = 0.0, s3 = 0.0; + + if (QD_ISINF(c0)) return; + + s0 = qd::quick_two_sum(c3, c4, c4); + s0 = qd::quick_two_sum(c2, s0, c3); + s0 = qd::quick_two_sum(c1, s0, c2); + c0 = qd::quick_two_sum(c0, s0, c1); + + s0 = c0; + s1 = c1; + + if (s1 != 0.0) { + s1 = qd::quick_two_sum(s1, c2, s2); + if (s2 != 0.0) { + s2 = qd::quick_two_sum(s2, c3, s3); + if (s3 != 0.0) + s3 += c4; + else + s2 = qd::quick_two_sum(s2, c4, s3); + } else { + s1 = qd::quick_two_sum(s1, c3, s2); + if (s2 != 0.0) + s2 = qd::quick_two_sum(s2, c4, s3); + else + s1 = qd::quick_two_sum(s1, c4, s2); + } + } else { + s0 = qd::quick_two_sum(s0, c2, s1); + if (s1 != 0.0) { + s1 = qd::quick_two_sum(s1, c3, s2); + if (s2 != 0.0) + s2 = qd::quick_two_sum(s2, c4, s3); + else + s1 = qd::quick_two_sum(s1, c4, s2); + } else { + s0 = qd::quick_two_sum(s0, c3, s1); + if (s1 != 0.0) + s1 = qd::quick_two_sum(s1, c4, s2); + else + s0 = qd::quick_two_sum(s0, c4, s1); + } + } + + c0 = s0; + c1 = s1; + c2 = s2; + c3 = s3; +} +} + +inline void qd_real::renorm() { + qd::renorm(x[0], x[1], x[2], x[3]); +} + +inline void qd_real::renorm(double &e) { + qd::renorm(x[0], x[1], x[2], x[3], e); +} + + +/********** Additions ************/ +namespace qd { + +inline void three_sum(double &a, double &b, double &c) { + double t1, t2, t3; + t1 = qd::two_sum(a, b, t2); + a = qd::two_sum(c, t1, t3); + b = qd::two_sum(t2, t3, c); +} + +inline void three_sum2(double &a, double &b, double &c) { + double t1, t2, t3; + t1 = qd::two_sum(a, b, t2); + a = qd::two_sum(c, t1, t3); + b = t2 + t3; +} + +} + +/* quad-double + double */ +inline qd_real operator+(const qd_real &a, double b) { + double c0, c1, c2, c3; + double e; + + c0 = qd::two_sum(a[0], b, e); + c1 = qd::two_sum(a[1], e, e); + c2 = qd::two_sum(a[2], e, e); + c3 = qd::two_sum(a[3], e, e); + + qd::renorm(c0, c1, c2, c3, e); + + return qd_real(c0, c1, c2, c3); +} + +/* quad-double + double-double */ +inline qd_real operator+(const qd_real &a, const dd_real &b) { + + double s0, s1, s2, s3; + double t0, t1; + + s0 = qd::two_sum(a[0], b._hi(), t0); + s1 = qd::two_sum(a[1], b._lo(), t1); + + s1 = qd::two_sum(s1, t0, t0); + + s2 = a[2]; + qd::three_sum(s2, t0, t1); + + s3 = qd::two_sum(t0, a[3], t0); + t0 += t1; + + qd::renorm(s0, s1, s2, s3, t0); + return qd_real(s0, s1, s2, s3); +} + + +/* double + quad-double */ +inline qd_real operator+(double a, const qd_real &b) { + return (b + a); +} + +/* double-double + quad-double */ +inline qd_real operator+(const dd_real &a, const qd_real &b) { + return (b + a); +} + +namespace qd { + +/* s = quick_three_accum(a, b, c) adds c to the dd-pair (a, b). + * If the result does not fit in two doubles, then the sum is + * output into s and (a,b) contains the remainder. Otherwise + * s is zero and (a,b) contains the sum. */ +inline double quick_three_accum(double &a, double &b, double c) { + double s; + bool za, zb; + + s = qd::two_sum(b, c, b); + s = qd::two_sum(a, s, a); + + za = (a != 0.0); + zb = (b != 0.0); + + if (za && zb) + return s; + + if (!zb) { + b = a; + a = s; + } else { + a = s; + } + + return 0.0; +} + +} + +inline qd_real qd_real::ieee_add(const qd_real &a, const qd_real &b) { + int i, j, k; + double s, t; + double u, v; /* double-length accumulator */ + double x[4] = {0.0, 0.0, 0.0, 0.0}; + + i = j = k = 0; + if (std::abs(a[i]) > std::abs(b[j])) + u = a[i++]; + else + u = b[j++]; + if (std::abs(a[i]) > std::abs(b[j])) + v = a[i++]; + else + v = b[j++]; + + u = qd::quick_two_sum(u, v, v); + + while (k < 4) { + if (i >= 4 && j >= 4) { + x[k] = u; + if (k < 3) + x[++k] = v; + break; + } + + if (i >= 4) + t = b[j++]; + else if (j >= 4) + t = a[i++]; + else if (std::abs(a[i]) > std::abs(b[j])) { + t = a[i++]; + } else + t = b[j++]; + + s = qd::quick_three_accum(u, v, t); + + if (s != 0.0) { + x[k++] = s; + } + } + + /* add the rest. */ + for (k = i; k < 4; k++) + x[3] += a[k]; + for (k = j; k < 4; k++) + x[3] += b[k]; + + qd::renorm(x[0], x[1], x[2], x[3]); + return qd_real(x[0], x[1], x[2], x[3]); +} + +inline qd_real qd_real::sloppy_add(const qd_real &a, const qd_real &b) { + /* + double s0, s1, s2, s3; + double t0, t1, t2, t3; + + s0 = qd::two_sum(a[0], b[0], t0); + s1 = qd::two_sum(a[1], b[1], t1); + s2 = qd::two_sum(a[2], b[2], t2); + s3 = qd::two_sum(a[3], b[3], t3); + + s1 = qd::two_sum(s1, t0, t0); + qd::three_sum(s2, t0, t1); + qd::three_sum2(s3, t0, t2); + t0 = t0 + t1 + t3; + + qd::renorm(s0, s1, s2, s3, t0); + return qd_real(s0, s1, s2, s3, t0); + */ + + /* Same as above, but addition re-organized to minimize + data dependency ... unfortunately some compilers are + not very smart to do this automatically */ + double s0, s1, s2, s3; + double t0, t1, t2, t3; + + double v0, v1, v2, v3; + double u0, u1, u2, u3; + double w0, w1, w2, w3; + + s0 = a[0] + b[0]; + s1 = a[1] + b[1]; + s2 = a[2] + b[2]; + s3 = a[3] + b[3]; + + v0 = s0 - a[0]; + v1 = s1 - a[1]; + v2 = s2 - a[2]; + v3 = s3 - a[3]; + + u0 = s0 - v0; + u1 = s1 - v1; + u2 = s2 - v2; + u3 = s3 - v3; + + w0 = a[0] - u0; + w1 = a[1] - u1; + w2 = a[2] - u2; + w3 = a[3] - u3; + + u0 = b[0] - v0; + u1 = b[1] - v1; + u2 = b[2] - v2; + u3 = b[3] - v3; + + t0 = w0 + u0; + t1 = w1 + u1; + t2 = w2 + u2; + t3 = w3 + u3; + + s1 = qd::two_sum(s1, t0, t0); + qd::three_sum(s2, t0, t1); + qd::three_sum2(s3, t0, t2); + t0 = t0 + t1 + t3; + + /* renormalize */ + qd::renorm(s0, s1, s2, s3, t0); + return qd_real(s0, s1, s2, s3); +} + +/* quad-double + quad-double */ +inline qd_real operator+(const qd_real &a, const qd_real &b) { +#ifndef QD_IEEE_ADD + return qd_real::sloppy_add(a, b); +#else + return qd_real::ieee_add(a, b); +#endif +} + + + +/********** Self-Additions ************/ +/* quad-double += double */ +inline qd_real &qd_real::operator+=(double a) { + *this = *this + a; + return *this; +} + +/* quad-double += double-double */ +inline qd_real &qd_real::operator+=(const dd_real &a) { + *this = *this + a; + return *this; +} + +/* quad-double += quad-double */ +inline qd_real &qd_real::operator+=(const qd_real &a) { + *this = *this + a; + return *this; +} + +/********** Unary Minus **********/ +inline qd_real qd_real::operator-() const { + return qd_real(-x[0], -x[1], -x[2], -x[3]); +} + +/********** Subtractions **********/ +inline qd_real operator-(const qd_real &a, double b) { + return (a + (-b)); +} + +inline qd_real operator-(double a, const qd_real &b) { + return (a + (-b)); +} + +inline qd_real operator-(const qd_real &a, const dd_real &b) { + return (a + (-b)); +} + +inline qd_real operator-(const dd_real &a, const qd_real &b) { + return (a + (-b)); +} + +inline qd_real operator-(const qd_real &a, const qd_real &b) { + return (a + (-b)); +} + +/********** Self-Subtractions **********/ +inline qd_real &qd_real::operator-=(double a) { + return ((*this) += (-a)); +} + +inline qd_real &qd_real::operator-=(const dd_real &a) { + return ((*this) += (-a)); +} + +inline qd_real &qd_real::operator-=(const qd_real &a) { + return ((*this) += (-a)); +} + + +inline qd_real operator*(double a, const qd_real &b) { + return (b * a); +} + +inline qd_real operator*(const dd_real &a, const qd_real &b) { + return (b * a); +} + +inline qd_real mul_pwr2(const qd_real &a, double b) { + return qd_real(a[0] * b, a[1] * b, a[2] * b, a[3] * b); +} + +/********** Multiplications **********/ +inline qd_real operator*(const qd_real &a, double b) { + double p0, p1, p2, p3; + double q0, q1, q2; + double s0, s1, s2, s3, s4; + + p0 = qd::two_prod(a[0], b, q0); + p1 = qd::two_prod(a[1], b, q1); + p2 = qd::two_prod(a[2], b, q2); + p3 = a[3] * b; + + s0 = p0; + + s1 = qd::two_sum(q0, p1, s2); + + qd::three_sum(s2, q1, p2); + + qd::three_sum2(q1, q2, p3); + s3 = q1; + + s4 = q2 + p2; + + qd::renorm(s0, s1, s2, s3, s4); + return qd_real(s0, s1, s2, s3); + +} + +/* quad-double * double-double */ +/* a0 * b0 0 + a0 * b1 1 + a1 * b0 2 + a1 * b1 3 + a2 * b0 4 + a2 * b1 5 + a3 * b0 6 + a3 * b1 7 */ +inline qd_real operator*(const qd_real &a, const dd_real &b) { + double p0, p1, p2, p3, p4; + double q0, q1, q2, q3, q4; + double s0, s1, s2; + double t0, t1; + + p0 = qd::two_prod(a[0], b._hi(), q0); + p1 = qd::two_prod(a[0], b._lo(), q1); + p2 = qd::two_prod(a[1], b._hi(), q2); + p3 = qd::two_prod(a[1], b._lo(), q3); + p4 = qd::two_prod(a[2], b._hi(), q4); + + qd::three_sum(p1, p2, q0); + + /* Five-Three-Sum */ + qd::three_sum(p2, p3, p4); + q1 = qd::two_sum(q1, q2, q2); + s0 = qd::two_sum(p2, q1, t0); + s1 = qd::two_sum(p3, q2, t1); + s1 = qd::two_sum(s1, t0, t0); + s2 = t0 + t1 + p4; + p2 = s0; + + p3 = a[2] * b._hi() + a[3] * b._lo() + q3 + q4; + qd::three_sum2(p3, q0, s1); + p4 = q0 + s2; + + qd::renorm(p0, p1, p2, p3, p4); + return qd_real(p0, p1, p2, p3); +} + +/* quad-double * quad-double */ +/* a0 * b0 0 + a0 * b1 1 + a1 * b0 2 + a0 * b2 3 + a1 * b1 4 + a2 * b0 5 + a0 * b3 6 + a1 * b2 7 + a2 * b1 8 + a3 * b0 9 */ +inline qd_real qd_real::sloppy_mul(const qd_real &a, const qd_real &b) { + double p0, p1, p2, p3, p4, p5; + double q0, q1, q2, q3, q4, q5; + double t0, t1; + double s0, s1, s2; + + p0 = qd::two_prod(a[0], b[0], q0); + + p1 = qd::two_prod(a[0], b[1], q1); + p2 = qd::two_prod(a[1], b[0], q2); + + p3 = qd::two_prod(a[0], b[2], q3); + p4 = qd::two_prod(a[1], b[1], q4); + p5 = qd::two_prod(a[2], b[0], q5); + + /* Start Accumulation */ + qd::three_sum(p1, p2, q0); + + /* Six-Three Sum of p2, q1, q2, p3, p4, p5. */ + qd::three_sum(p2, q1, q2); + qd::three_sum(p3, p4, p5); + /* compute (s0, s1, s2) = (p2, q1, q2) + (p3, p4, p5). */ + s0 = qd::two_sum(p2, p3, t0); + s1 = qd::two_sum(q1, p4, t1); + s2 = q2 + p5; + s1 = qd::two_sum(s1, t0, t0); + s2 += (t0 + t1); + + /* O(eps^3) order terms */ + s1 += a[0]*b[3] + a[1]*b[2] + a[2]*b[1] + a[3]*b[0] + q0 + q3 + q4 + q5; + qd::renorm(p0, p1, s0, s1, s2); + return qd_real(p0, p1, s0, s1); +} + +inline qd_real qd_real::accurate_mul(const qd_real &a, const qd_real &b) { + double p0, p1, p2, p3, p4, p5; + double q0, q1, q2, q3, q4, q5; + double p6, p7, p8, p9; + double q6, q7, q8, q9; + double r0, r1; + double t0, t1; + double s0, s1, s2; + + p0 = qd::two_prod(a[0], b[0], q0); + + p1 = qd::two_prod(a[0], b[1], q1); + p2 = qd::two_prod(a[1], b[0], q2); + + p3 = qd::two_prod(a[0], b[2], q3); + p4 = qd::two_prod(a[1], b[1], q4); + p5 = qd::two_prod(a[2], b[0], q5); + + /* Start Accumulation */ + qd::three_sum(p1, p2, q0); + + /* Six-Three Sum of p2, q1, q2, p3, p4, p5. */ + qd::three_sum(p2, q1, q2); + qd::three_sum(p3, p4, p5); + /* compute (s0, s1, s2) = (p2, q1, q2) + (p3, p4, p5). */ + s0 = qd::two_sum(p2, p3, t0); + s1 = qd::two_sum(q1, p4, t1); + s2 = q2 + p5; + s1 = qd::two_sum(s1, t0, t0); + s2 += (t0 + t1); + + /* O(eps^3) order terms */ + p6 = qd::two_prod(a[0], b[3], q6); + p7 = qd::two_prod(a[1], b[2], q7); + p8 = qd::two_prod(a[2], b[1], q8); + p9 = qd::two_prod(a[3], b[0], q9); + + /* Nine-Two-Sum of q0, s1, q3, q4, q5, p6, p7, p8, p9. */ + q0 = qd::two_sum(q0, q3, q3); + q4 = qd::two_sum(q4, q5, q5); + p6 = qd::two_sum(p6, p7, p7); + p8 = qd::two_sum(p8, p9, p9); + /* Compute (t0, t1) = (q0, q3) + (q4, q5). */ + t0 = qd::two_sum(q0, q4, t1); + t1 += (q3 + q5); + /* Compute (r0, r1) = (p6, p7) + (p8, p9). */ + r0 = qd::two_sum(p6, p8, r1); + r1 += (p7 + p9); + /* Compute (q3, q4) = (t0, t1) + (r0, r1). */ + q3 = qd::two_sum(t0, r0, q4); + q4 += (t1 + r1); + /* Compute (t0, t1) = (q3, q4) + s1. */ + t0 = qd::two_sum(q3, s1, t1); + t1 += q4; + + /* O(eps^4) terms -- Nine-One-Sum */ + t1 += a[1] * b[3] + a[2] * b[2] + a[3] * b[1] + q6 + q7 + q8 + q9 + s2; + + qd::renorm(p0, p1, s0, t0, t1); + return qd_real(p0, p1, s0, t0); +} + +inline qd_real operator*(const qd_real &a, const qd_real &b) { +#ifdef QD_SLOPPY_MUL + return qd_real::sloppy_mul(a, b); +#else + return qd_real::accurate_mul(a, b); +#endif +} + +/* quad-double ^ 2 = (x0 + x1 + x2 + x3) ^ 2 + = x0 ^ 2 + 2 x0 * x1 + (2 x0 * x2 + x1 ^ 2) + + (2 x0 * x3 + 2 x1 * x2) */ +inline qd_real sqr(const qd_real &a) { + double p0, p1, p2, p3, p4, p5; + double q0, q1, q2, q3; + double s0, s1; + double t0, t1; + + p0 = qd::two_sqr(a[0], q0); + p1 = qd::two_prod(2.0 * a[0], a[1], q1); + p2 = qd::two_prod(2.0 * a[0], a[2], q2); + p3 = qd::two_sqr(a[1], q3); + + p1 = qd::two_sum(q0, p1, q0); + + q0 = qd::two_sum(q0, q1, q1); + p2 = qd::two_sum(p2, p3, p3); + + s0 = qd::two_sum(q0, p2, t0); + s1 = qd::two_sum(q1, p3, t1); + + s1 = qd::two_sum(s1, t0, t0); + t0 += t1; + + s1 = qd::quick_two_sum(s1, t0, t0); + p2 = qd::quick_two_sum(s0, s1, t1); + p3 = qd::quick_two_sum(t1, t0, q0); + + p4 = 2.0 * a[0] * a[3]; + p5 = 2.0 * a[1] * a[2]; + + p4 = qd::two_sum(p4, p5, p5); + q2 = qd::two_sum(q2, q3, q3); + + t0 = qd::two_sum(p4, q2, t1); + t1 = t1 + p5 + q3; + + p3 = qd::two_sum(p3, t0, p4); + p4 = p4 + q0 + t1; + + qd::renorm(p0, p1, p2, p3, p4); + return qd_real(p0, p1, p2, p3); + +} + +/********** Self-Multiplication **********/ +/* quad-double *= double */ +inline qd_real &qd_real::operator*=(double a) { + *this = (*this * a); + return *this; +} + +/* quad-double *= double-double */ +inline qd_real &qd_real::operator*=(const dd_real &a) { + *this = (*this * a); + return *this; +} + +/* quad-double *= quad-double */ +inline qd_real &qd_real::operator*=(const qd_real &a) { + *this = *this * a; + return *this; +} + +inline qd_real operator/ (const qd_real &a, const dd_real &b) { +#ifdef QD_SLOPPY_DIV + return qd_real::sloppy_div(a, b); +#else + return qd_real::accurate_div(a, b); +#endif +} + +inline qd_real operator/(const qd_real &a, const qd_real &b) { +#ifdef QD_SLOPPY_DIV + return qd_real::sloppy_div(a, b); +#else + return qd_real::accurate_div(a, b); +#endif +} + +/* double / quad-double */ +inline qd_real operator/(double a, const qd_real &b) { + return qd_real(a) / b; +} + +/* double-double / quad-double */ +inline qd_real operator/(const dd_real &a, const qd_real &b) { + return qd_real(a) / b; +} + +/********** Self-Divisions **********/ +/* quad-double /= double */ +inline qd_real &qd_real::operator/=(double a) { + *this = (*this / a); + return *this; +} + +/* quad-double /= double-double */ +inline qd_real &qd_real::operator/=(const dd_real &a) { + *this = (*this / a); + return *this; +} + +/* quad-double /= quad-double */ +inline qd_real &qd_real::operator/=(const qd_real &a) { + *this = (*this / a); + return *this; +} + + +/********** Exponentiation **********/ +inline qd_real qd_real::operator^(int n) const { + return pow(*this, n); +} + +/********** Miscellaneous **********/ +inline qd_real abs(const qd_real &a) { + return (a[0] < 0.0) ? -a : a; +} + +inline qd_real fabs(const qd_real &a) { + return abs(a); +} + +/* Quick version. May be off by one when qd is very close + to the middle of two integers. */ +inline qd_real quick_nint(const qd_real &a) { + qd_real r = qd_real(qd::nint(a[0]), qd::nint(a[1]), + qd::nint(a[2]), qd::nint(a[3])); + r.renorm(); + return r; +} + +/*********** Assignments ************/ +/* quad-double = double */ +inline qd_real &qd_real::operator=(double a) { + x[0] = a; + x[1] = x[2] = x[3] = 0.0; + return *this; +} + +/* quad-double = double-double */ +inline qd_real &qd_real::operator=(const dd_real &a) { + x[0] = a._hi(); + x[1] = a._lo(); + x[2] = x[3] = 0.0; + return *this; +} + +/********** Equality Comparison **********/ +inline bool operator==(const qd_real &a, double b) { + return (a[0] == b && a[1] == 0.0 && a[2] == 0.0 && a[3] == 0.0); +} + +inline bool operator==(double a, const qd_real &b) { + return (b == a); +} + +inline bool operator==(const qd_real &a, const dd_real &b) { + return (a[0] == b._hi() && a[1] == b._lo() && + a[2] == 0.0 && a[3] == 0.0); +} + +inline bool operator==(const dd_real &a, const qd_real &b) { + return (b == a); +} + +inline bool operator==(const qd_real &a, const qd_real &b) { + return (a[0] == b[0] && a[1] == b[1] && + a[2] == b[2] && a[3] == b[3]); +} + + +/********** Less-Than Comparison ***********/ +inline bool operator<(const qd_real &a, double b) { + return (a[0] < b || (a[0] == b && a[1] < 0.0)); +} + +inline bool operator<(double a, const qd_real &b) { + return (b > a); +} + +inline bool operator<(const qd_real &a, const dd_real &b) { + return (a[0] < b._hi() || + (a[0] == b._hi() && (a[1] < b._lo() || + (a[1] == b._lo() && a[2] < 0.0)))); +} + +inline bool operator<(const dd_real &a, const qd_real &b) { + return (b > a); +} + +inline bool operator<(const qd_real &a, const qd_real &b) { + return (a[0] < b[0] || + (a[0] == b[0] && (a[1] < b[1] || + (a[1] == b[1] && (a[2] < b[2] || + (a[2] == b[2] && a[3] < b[3])))))); +} + +/********** Greater-Than Comparison ***********/ +inline bool operator>(const qd_real &a, double b) { + return (a[0] > b || (a[0] == b && a[1] > 0.0)); +} + +inline bool operator>(double a, const qd_real &b) { + return (b < a); +} + +inline bool operator>(const qd_real &a, const dd_real &b) { + return (a[0] > b._hi() || + (a[0] == b._hi() && (a[1] > b._lo() || + (a[1] == b._lo() && a[2] > 0.0)))); +} + +inline bool operator>(const dd_real &a, const qd_real &b) { + return (b < a); +} + +inline bool operator>(const qd_real &a, const qd_real &b) { + return (a[0] > b[0] || + (a[0] == b[0] && (a[1] > b[1] || + (a[1] == b[1] && (a[2] > b[2] || + (a[2] == b[2] && a[3] > b[3])))))); +} + + +/********** Less-Than-Or-Equal-To Comparison **********/ +inline bool operator<=(const qd_real &a, double b) { + return (a[0] < b || (a[0] == b && a[1] <= 0.0)); +} + +inline bool operator<=(double a, const qd_real &b) { + return (b >= a); +} + +inline bool operator<=(const qd_real &a, const dd_real &b) { + return (a[0] < b._hi() || + (a[0] == b._hi() && (a[1] < b._lo() || + (a[1] == b._lo() && a[2] <= 0.0)))); +} + +inline bool operator<=(const dd_real &a, const qd_real &b) { + return (b >= a); +} + +inline bool operator<=(const qd_real &a, const qd_real &b) { + return (a[0] < b[0] || + (a[0] == b[0] && (a[1] < b[1] || + (a[1] == b[1] && (a[2] < b[2] || + (a[2] == b[2] && a[3] <= b[3])))))); +} + +/********** Greater-Than-Or-Equal-To Comparison **********/ +inline bool operator>=(const qd_real &a, double b) { + return (a[0] > b || (a[0] == b && a[1] >= 0.0)); +} + +inline bool operator>=(double a, const qd_real &b) { + return (b <= a); +} + +inline bool operator>=(const qd_real &a, const dd_real &b) { + return (a[0] > b._hi() || + (a[0] == b._hi() && (a[1] > b._lo() || + (a[1] == b._lo() && a[2] >= 0.0)))); +} + +inline bool operator>=(const dd_real &a, const qd_real &b) { + return (b <= a); +} + +inline bool operator>=(const qd_real &a, const qd_real &b) { + return (a[0] > b[0] || + (a[0] == b[0] && (a[1] > b[1] || + (a[1] == b[1] && (a[2] > b[2] || + (a[2] == b[2] && a[3] >= b[3])))))); +} + + + +/********** Not-Equal-To Comparison **********/ +inline bool operator!=(const qd_real &a, double b) { + return !(a == b); +} + +inline bool operator!=(double a, const qd_real &b) { + return !(a == b); +} + +inline bool operator!=(const qd_real &a, const dd_real &b) { + return !(a == b); +} + +inline bool operator!=(const dd_real &a, const qd_real &b) { + return !(a == b); +} + +inline bool operator!=(const qd_real &a, const qd_real &b) { + return !(a == b); +} + + + +inline qd_real aint(const qd_real &a) { + return (a[0] >= 0) ? floor(a) : ceil(a); +} + +inline bool qd_real::is_zero() const { + return (x[0] == 0.0); +} + +inline bool qd_real::is_one() const { + return (x[0] == 1.0 && x[1] == 0.0 && x[2] == 0.0 && x[3] == 0.0); +} + +inline bool qd_real::is_positive() const { + return (x[0] > 0.0); +} + +inline bool qd_real::is_negative() const { + return (x[0] < 0.0); +} + +inline qd_real::operator bool() const { + return (x[0] != 0.0); +} + +inline qd_real::operator double() const { + return to_double(*this); +} + +inline dd_real to_dd_real(const qd_real &a) { + return dd_real(a[0], a[1]); +} + +inline double to_double(const qd_real &a) { + return a[0]; +} + +inline int to_int(const qd_real &a) { + return static_cast(a[0]); +} + +inline qd_real inv(const qd_real &qd) { + return 1.0 / qd; +} + +inline qd_real max(const qd_real &a, const qd_real &b) { + return (a > b) ? a : b; +} + +inline qd_real max(const qd_real &a, const qd_real &b, + const qd_real &c) { + return (a > b) ? ((a > c) ? a : c) : ((b > c) ? b : c); +} + +inline qd_real min(const qd_real &a, const qd_real &b) { + return (a < b) ? a : b; +} + +inline qd_real min(const qd_real &a, const qd_real &b, + const qd_real &c) { + return (a < b) ? ((a < c) ? a : c) : ((b < c) ? b : c); +} + +/* Random number generator */ +inline qd_real qd_real::rand() { + return qdrand(); +} + +inline qd_real ldexp(const qd_real &a, int n) { + return qd_real(std::ldexp(a[0], n), std::ldexp(a[1], n), + std::ldexp(a[2], n), std::ldexp(a[3], n)); +} + +#endif /* _QD_QD_INLINE_H */ diff --git a/external/PackedCSparse/qd/qd_real.cc b/external/PackedCSparse/qd/qd_real.cc new file mode 100644 index 000000000..02cb7aa3b --- /dev/null +++ b/external/PackedCSparse/qd/qd_real.cc @@ -0,0 +1,2624 @@ +/* + * src/qd_real.cc + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2007 + * + * Contains implementation of non-inlined functions of quad-double + * package. Inlined functions are found in qd_inline.h (in include directory). + */ +#include +#include +#include +#include +#include +#include +#include + +#include "qd_config.h" +#include "qd_real.h" +#include "util.h" + +#include "bits.h" + +#ifndef QD_INLINE +#include "qd_inline.h" +#endif + +using std::cout; +using std::cerr; +using std::endl; +using std::istream; +using std::ostream; +using std::ios_base; +using std::string; +using std::setw; + +using namespace qd; + +void qd_real::error(const char *msg) { + //if (msg) { cerr << "ERROR " << msg << endl; } +} + +/********** Multiplications **********/ + +qd_real nint(const qd_real &a) { + double x0, x1, x2, x3; + + x0 = nint(a[0]); + x1 = x2 = x3 = 0.0; + + if (x0 == a[0]) { + /* First double is already an integer. */ + x1 = nint(a[1]); + + if (x1 == a[1]) { + /* Second double is already an integer. */ + x2 = nint(a[2]); + + if (x2 == a[2]) { + /* Third double is already an integer. */ + x3 = nint(a[3]); + } else { + if (std::abs(x2 - a[2]) == 0.5 && a[3] < 0.0) { + x2 -= 1.0; + } + } + + } else { + if (std::abs(x1 - a[1]) == 0.5 && a[2] < 0.0) { + x1 -= 1.0; + } + } + + } else { + /* First double is not an integer. */ + if (std::abs(x0 - a[0]) == 0.5 && a[1] < 0.0) { + x0 -= 1.0; + } + } + + renorm(x0, x1, x2, x3); + return qd_real(x0, x1, x2, x3); +} + +qd_real floor(const qd_real &a) { + double x0, x1, x2, x3; + x1 = x2 = x3 = 0.0; + x0 = std::floor(a[0]); + + if (x0 == a[0]) { + x1 = std::floor(a[1]); + + if (x1 == a[1]) { + x2 = std::floor(a[2]); + + if (x2 == a[2]) { + x3 = std::floor(a[3]); + } + } + + renorm(x0, x1, x2, x3); + return qd_real(x0, x1, x2, x3); + } + + return qd_real(x0, x1, x2, x3); +} + +qd_real ceil(const qd_real &a) { + double x0, x1, x2, x3; + x1 = x2 = x3 = 0.0; + x0 = std::ceil(a[0]); + + if (x0 == a[0]) { + x1 = std::ceil(a[1]); + + if (x1 == a[1]) { + x2 = std::ceil(a[2]); + + if (x2 == a[2]) { + x3 = std::ceil(a[3]); + } + } + + renorm(x0, x1, x2, x3); + return qd_real(x0, x1, x2, x3); + } + + return qd_real(x0, x1, x2, x3); +} + + + +/********** Divisions **********/ +/* quad-double / double */ +qd_real operator/(const qd_real &a, double b) { + /* Strategy: compute approximate quotient using high order + doubles, and then correct it 3 times using the remainder. + (Analogous to long division.) */ + double t0, t1; + double q0, q1, q2, q3; + qd_real r; + + q0 = a[0] / b; /* approximate quotient */ + + /* Compute the remainder a - q0 * b */ + t0 = two_prod(q0, b, t1); + r = a - dd_real(t0, t1); + + /* Compute the first correction */ + q1 = r[0] / b; + t0 = two_prod(q1, b, t1); + r -= dd_real(t0, t1); + + /* Second correction to the quotient. */ + q2 = r[0] / b; + t0 = two_prod(q2, b, t1); + r -= dd_real(t0, t1); + + /* Final correction to the quotient. */ + q3 = r[0] / b; + + renorm(q0, q1, q2, q3); + return qd_real(q0, q1, q2, q3); +} + +qd_real::qd_real(const char *s) { + if (qd_real::read(s, *this)) { + qd_real::error("(qd_real::qd_real): INPUT ERROR."); + *this = qd_real::_nan; + } +} + +qd_real &qd_real::operator=(const char *s) { + if (qd_real::read(s, *this)) { + qd_real::error("(qd_real::operator=): INPUT ERROR."); + *this = qd_real::_nan; + } + return *this; +} + +istream &operator>>(istream &s, qd_real &qd) { + char str[255]; + s >> str; + qd = qd_real(str); + return s; +} + +ostream &operator<<(ostream &os, const qd_real &qd) { + bool showpos = (os.flags() & ios_base::showpos) != 0; + bool uppercase = (os.flags() & ios_base::uppercase) != 0; + return os << qd.to_string((int)os.precision(), (int)os.width(), os.flags(), + showpos, uppercase, os.fill()); +} + +/* Read a quad-double from s. */ +int qd_real::read(const char *s, qd_real &qd) { + const char *p = s; + char ch; + int sign = 0; + int point = -1; /* location of decimal point */ + int nd = 0; /* number of digits read */ + int e = 0; /* exponent. */ + bool done = false; + qd_real r = 0.0; /* number being read */ + + /* Skip any leading spaces */ + while (*p == ' ') p++; + + while (!done && (ch = *p) != '\0') { + if (ch >= '0' && ch <= '9') { + /* It's a digit */ + int d = ch - '0'; + r *= 10.0; + r += static_cast(d); + nd++; + } else { + /* Non-digit */ + switch (ch) { + case '.': + if (point >= 0) + return -1; /* we've already encountered a decimal point. */ + point = nd; + break; + case '-': + case '+': + if (sign != 0 || nd > 0) + return -1; /* we've already encountered a sign, or if its + not at first position. */ + sign = (ch == '-') ? -1 : 1; + break; + case 'E': + case 'e': + int nread; + nread = std::sscanf(p+1, "%d", &e); + done = true; + if (nread != 1) + return -1; /* read of exponent failed. */ + break; + case ' ': + done = true; + break; + default: + return -1; + + } + } + + p++; + } + + + + /* Adjust exponent to account for decimal point */ + if (point >= 0) { + e -= (nd - point); + } + + /* Multiply the the exponent */ + if (e != 0) { + r *= (qd_real(10.0) ^ e); + } + + qd = (sign < 0) ? -r : r; + return 0; +} + +void qd_real::to_digits(char *s, int &expn, int precision) const { + int D = precision + 1; /* number of digits to compute */ + + qd_real r = abs(*this); + int e; /* exponent */ + int i, d; + + if (x[0] == 0.0) { + /* this == 0.0 */ + expn = 0; + for (i = 0; i < precision; i++) s[i] = '0'; + return; + } + + /* First determine the (approximate) exponent. */ + e = static_cast(std::floor(std::log10(std::abs(x[0])))); + + if (e < -300) { + r *= qd_real(10.0) ^ 300; + r /= qd_real(10.0) ^ (e + 300); + } else if (e > 300) { + r = ldexp(r, -53); + r /= qd_real(10.0) ^ e; + r = ldexp(r, 53); + } else { + r /= qd_real(10.0) ^ e; + } + + /* Fix exponent if we are off by one */ + if (r >= 10.0) { + r /= 10.0; + e++; + } else if (r < 1.0) { + r *= 10.0; + e--; + } + + if (r >= 10.0 || r < 1.0) { + qd_real::error("(qd_real::to_digits): can't compute exponent."); + return; + } + + /* Extract the digits */ + for (i = 0; i < D; i++) { + d = static_cast(r[0]); + r -= d; + r *= 10.0; + + s[i] = static_cast(d + '0'); + } + + /* Fix out of range digits. */ + for (i = D-1; i > 0; i--) { + if (s[i] < '0') { + s[i-1]--; + s[i] += 10; + } else if (s[i] > '9') { + s[i-1]++; + s[i] -= 10; + } + } + + if (s[0] <= '0') { + qd_real::error("(qd_real::to_digits): non-positive leading digit."); + return; + } + + /* Round, handle carry */ + if (s[D-1] >= '5') { + s[D-2]++; + + i = D-2; + while (i > 0 && s[i] > '9') { + s[i] -= 10; + s[--i]++; + } + } + + /* If first digit is 10, shift everything. */ + if (s[0] > '9') { + e++; + for (i = precision; i >= 2; i--) s[i] = s[i-1]; + s[0] = '1'; + s[1] = '0'; + } + + s[precision] = 0; + expn = e; +} + +/* Writes the quad-double number into the character array s of length len. + The integer d specifies how many significant digits to write. + The string s must be able to hold at least (d+8) characters. + showpos indicates whether to use the + sign, and uppercase indicates + whether the E or e is to be used for the exponent. */ +void qd_real::write(char *s, int len, int precision, + bool showpos, bool uppercase) const { + string str = to_string(precision, 0, ios_base::scientific, showpos, uppercase); + strncpy(s, str.c_str(), len-1); + s[len-1] = 0; +} + +void round_string_qd(char *s, int precision, int *offset){ + /* + Input string must be all digits or errors will occur. + */ + + int i; + int D = precision ; + + /* Round, handle carry */ + if (D>0 && s[D] >= '5') { + s[D-1]++; + + i = D-1; + while (i > 0 && s[i] > '9') { + s[i] -= 10; + s[--i]++; + } + } + + /* If first digit is 10, shift everything. */ + if (s[0] > '9') { + // e++; // don't modify exponent here + for (i = precision; i >= 1; i--) s[i+1] = s[i]; + s[0] = '1'; + s[1] = '0'; + + (*offset)++ ; // now offset needs to be increased by one + precision++ ; + } + + s[precision] = 0; // add terminator for array +} + + +string qd_real::to_string(int precision, int width, ios_base::fmtflags fmt, + bool showpos, bool uppercase, char fill) const { + string s; + bool fixed = (fmt & ios_base::fixed) != 0; + bool sgn = true; + int i, e = 0; + + if (isinf()) { + if (*this < 0.0) + s += '-'; + else if (showpos) + s += '+'; + else + sgn = false; + s += uppercase ? "INF" : "inf"; + } else if (isnan()) { + s = uppercase ? "NAN" : "nan"; + sgn = false; + } else { + if (*this < 0.0) + s += '-'; + else if (showpos) + s += '+'; + else + sgn = false; + + if (*this == 0.0) { + /* Zero case */ + s += '0'; + if (precision > 0) { + s += '.'; + s.append(precision, '0'); + } + } else { + /* Non-zero case */ + int off = (fixed ? (1 + to_int(floor(log10(abs(*this))))) : 1); + int d = precision + off; + + int d_with_extra = d; + if(fixed) + d_with_extra = std::max(120, d); // longer than the max accuracy for DD + + // highly special case - fixed mode, precision is zero, abs(*this) < 1.0 + // without this trap a number like 0.9 printed fixed with 0 precision prints as 0 + // should be rounded to 1. + if(fixed && (precision == 0) && (abs(*this) < 1.0)){ + if(abs(*this) >= 0.5) + s += '1'; + else + s += '0'; + + return s; + } + + // handle near zero to working precision (but not exactly zero) + if (fixed && d <= 0) { + s += '0'; + if (precision > 0) { + s += '.'; + s.append(precision, '0'); + } + } else { // default + + char *t ; // = new char[d+1]; + int j; + + if(fixed){ + t = new char[d_with_extra+1]; + to_digits(t, e, d_with_extra); + } + else{ + t = new char[d+1]; + to_digits(t, e, d); + } + + off = e + 1; + + if (fixed) { + // fix the string if it's been computed incorrectly + // round here in the decimal string if required + round_string_qd(t, d, &off); + + if (off > 0) { + for (i = 0; i < off; i++) s += t[i]; + if (precision > 0) { + s += '.'; + for (j = 0; j < precision; j++, i++) s += t[i]; + } + } else { + s += "0."; + if (off < 0) s.append(-off, '0'); + for (i = 0; i < d; i++) s += t[i]; + } + } else { + s += t[0]; + if (precision > 0) s += '.'; + + for (i = 1; i <= precision; i++) + s += t[i]; + + } + delete [] t; + } + } + + // trap for improper offset with large values + // without this trap, output of values of the for 10^j - 1 fail for j > 28 + // and are output with the point in the wrong place, leading to a dramatically off value + if(fixed && (precision > 0)){ + // make sure that the value isn't dramatically larger + double from_string = atof(s.c_str()); + + // if this ratio is large, then we've got problems + if( fabs( from_string / this->x[0] ) > 3.0 ){ + + // loop on the string, find the point, move it up one + // don't act on the first character + for(i=1; i < (int)s.length(); i++){ + if(s[i] == '.'){ + s[i] = s[i-1] ; + s[i-1] = '.' ; + break; + } + } + + from_string = atof(s.c_str()); + // if this ratio is large, then the string has not been fixed + if( fabs( from_string / this->x[0] ) > 3.0 ){ + dd_real::error("Re-rounding unsuccessful in large number fixed point trap.") ; + } + } + } + + if (!fixed) { + /* Fill in exponent part */ + s += uppercase ? 'E' : 'e'; + append_expn(s, e); + } + } + + /* Fill in the blanks */ + size_t len = s.length(); + if (len < width) { + int delta = width - len; + if (fmt & ios_base::internal) { + if (sgn) + s.insert(static_cast(1), delta, fill); + else + s.insert(static_cast(0), delta, fill); + } else if (fmt & ios_base::left) { + s.append(delta, fill); + } else { + s.insert(static_cast(0), delta, fill); + } + } + + return s; +} + +/* Computes qd^n, where n is an integer. */ +qd_real pow(const qd_real &a, int n) { + if (n == 0) + return 1.0; + + qd_real r = a; /* odd-case multiplier */ + qd_real s = 1.0; /* current answer */ + int N = std::abs(n); + + if (N > 1) { + + /* Use binary exponentiation. */ + while (N > 0) { + if (N % 2 == 1) { + /* If odd, multiply by r. Note eventually N = 1, so this + eventually executes. */ + s *= r; + } + N /= 2; + if (N > 0) + r = sqr(r); + } + + } else { + s = r; + } + + if (n < 0) + return (1.0 / s); + + return s; +} + +qd_real pow(const qd_real &a, const qd_real &b) { + return exp(b * log(a)); +} + +qd_real npwr(const qd_real &a, int n) { + return pow(a, n); +} + +/* Debugging routines */ +void qd_real::dump_bits(const string &name, std::ostream &os) const { + string::size_type len = name.length(); + if (len > 0) { + os << name << " = "; + len += 3; + } + os << "[ "; + len += 2; + for (int j = 0; j < 4; j++) { + if (j > 0) for (string::size_type i = 0; i < len; i++) os << ' '; + print_double_info(os, x[j]); + if (j < 3) + os << endl; + else + os << " ]" << endl; + } +} + +void qd_real::dump(const string &name, std::ostream &os) const { + std::ios_base::fmtflags old_flags = os.flags(); + std::streamsize old_prec = os.precision(19); + os << std::scientific; + + string::size_type len = name.length(); + if (len > 0) { + os << name << " = "; + len += 3; + } + os << "[ "; + len += 2; + os << setw(27) << x[0] << ", " << setw(26) << x[1] << "," << endl; + for (string::size_type i = 0; i < len; i++) os << ' '; + os << setw(27) << x[2] << ", " << setw(26) << x[3] << " ]" << endl; + + os.precision(old_prec); + os.flags(old_flags); +} + +/* Divisions */ +/* quad-double / double-double */ +qd_real qd_real::sloppy_div(const qd_real &a, const dd_real &b) { + double q0, q1, q2, q3; + qd_real r; + qd_real qd_b(b); + + q0 = a[0] / b._hi(); + r = a - q0 * qd_b; + + q1 = r[0] / b._hi(); + r -= (q1 * qd_b); + + q2 = r[0] / b._hi(); + r -= (q2 * qd_b); + + q3 = r[0] / b._hi(); + + ::renorm(q0, q1, q2, q3); + return qd_real(q0, q1, q2, q3); +} + +qd_real qd_real::accurate_div(const qd_real &a, const dd_real &b) { + double q0, q1, q2, q3, q4; + qd_real r; + qd_real qd_b(b); + + q0 = a[0] / b._hi(); + r = a - q0 * qd_b; + + q1 = r[0] / b._hi(); + r -= (q1 * qd_b); + + q2 = r[0] / b._hi(); + r -= (q2 * qd_b); + + q3 = r[0] / b._hi(); + r -= (q3 * qd_b); + + q4 = r[0] / b._hi(); + + ::renorm(q0, q1, q2, q3, q4); + return qd_real(q0, q1, q2, q3); +} + +/* quad-double / quad-double */ +qd_real qd_real::sloppy_div(const qd_real &a, const qd_real &b) { + double q0, q1, q2, q3; + + qd_real r; + + q0 = a[0] / b[0]; + r = a - (b * q0); + + q1 = r[0] / b[0]; + r -= (b * q1); + + q2 = r[0] / b[0]; + r -= (b * q2); + + q3 = r[0] / b[0]; + + ::renorm(q0, q1, q2, q3); + + return qd_real(q0, q1, q2, q3); +} + +qd_real qd_real::accurate_div(const qd_real &a, const qd_real &b) { + double q0, q1, q2, q3; + + qd_real r; + + q0 = a[0] / b[0]; + r = a - (b * q0); + + q1 = r[0] / b[0]; + r -= (b * q1); + + q2 = r[0] / b[0]; + r -= (b * q2); + + q3 = r[0] / b[0]; + + r -= (b * q3); + double q4 = r[0] / b[0]; + + ::renorm(q0, q1, q2, q3, q4); + + return qd_real(q0, q1, q2, q3); +} + +QD_API qd_real sqrt(const qd_real &a) { + /* Strategy: + + Perform the following Newton iteration: + + x' = x + (1 - a * x^2) * x / 2; + + which converges to 1/sqrt(a), starting with the + double precision approximation to 1/sqrt(a). + Since Newton's iteration more or less doubles the + number of correct digits, we only need to perform it + twice. + */ + + if (a.is_zero()) + return 0.0; + + if (a.is_negative()) { + qd_real::error("(qd_real::sqrt): Negative argument."); + return qd_real::_nan; + } + + qd_real r = (1.0 / std::sqrt(a[0])); + qd_real h = mul_pwr2(a, 0.5); + + r += ((0.5 - h * sqr(r)) * r); + r += ((0.5 - h * sqr(r)) * r); + r += ((0.5 - h * sqr(r)) * r); + + r *= a; + return r; +} + + +/* Computes the n-th root of a */ +qd_real nroot(const qd_real &a, int n) { + /* Strategy: Use Newton's iteration to solve + + 1/(x^n) - a = 0 + + Newton iteration becomes + + x' = x + x * (1 - a * x^n) / n + + Since Newton's iteration converges quadratically, + we only need to perform it twice. + + */ + if (n <= 0) { + qd_real::error("(qd_real::nroot): N must be positive."); + return qd_real::_nan; + } + + if (n % 2 == 0 && a.is_negative()) { + qd_real::error("(qd_real::nroot): Negative argument."); + return qd_real::_nan; + } + + if (n == 1) { + return a; + } + if (n == 2) { + return sqrt(a); + } + if (a.is_zero()) { + return qd_real(0.0); + } + + + /* Note a^{-1/n} = exp(-log(a)/n) */ + qd_real r = abs(a); + qd_real x = std::exp(-std::log(r.x[0]) / n); + + /* Perform Newton's iteration. */ + double dbl_n = static_cast(n); + x += x * (1.0 - r * npwr(x, n)) / dbl_n; + x += x * (1.0 - r * npwr(x, n)) / dbl_n; + x += x * (1.0 - r * npwr(x, n)) / dbl_n; + if (a[0] < 0.0){ + x = -x; + } + return 1.0 / x; +} + +static const int n_inv_fact = 15; +static const qd_real inv_fact[n_inv_fact] = { + qd_real( 1.66666666666666657e-01, 9.25185853854297066e-18, + 5.13581318503262866e-34, 2.85094902409834186e-50), + qd_real( 4.16666666666666644e-02, 2.31296463463574266e-18, + 1.28395329625815716e-34, 7.12737256024585466e-51), + qd_real( 8.33333333333333322e-03, 1.15648231731787138e-19, + 1.60494162032269652e-36, 2.22730392507682967e-53), + qd_real( 1.38888888888888894e-03, -5.30054395437357706e-20, + -1.73868675534958776e-36, -1.63335621172300840e-52), + qd_real( 1.98412698412698413e-04, 1.72095582934207053e-22, + 1.49269123913941271e-40, 1.29470326746002471e-58), + qd_real( 2.48015873015873016e-05, 2.15119478667758816e-23, + 1.86586404892426588e-41, 1.61837908432503088e-59), + qd_real( 2.75573192239858925e-06, -1.85839327404647208e-22, + 8.49175460488199287e-39, -5.72661640789429621e-55), + qd_real( 2.75573192239858883e-07, 2.37677146222502973e-23, + -3.26318890334088294e-40, 1.61435111860404415e-56), + qd_real( 2.50521083854417202e-08, -1.44881407093591197e-24, + 2.04267351467144546e-41, -8.49632672007163175e-58), + qd_real( 2.08767569878681002e-09, -1.20734505911325997e-25, + 1.70222792889287100e-42, 1.41609532150396700e-58), + qd_real( 1.60590438368216133e-10, 1.25852945887520981e-26, + -5.31334602762985031e-43, 3.54021472597605528e-59), + qd_real( 1.14707455977297245e-11, 2.06555127528307454e-28, + 6.88907923246664603e-45, 5.72920002655109095e-61), + qd_real( 7.64716373181981641e-13, 7.03872877733453001e-30, + -7.82753927716258345e-48, 1.92138649443790242e-64), + qd_real( 4.77947733238738525e-14, 4.39920548583408126e-31, + -4.89221204822661465e-49, 1.20086655902368901e-65), + qd_real( 2.81145725434552060e-15, 1.65088427308614326e-31, + -2.87777179307447918e-50, 4.27110689256293549e-67) +}; + +qd_real exp(const qd_real &a) { + /* Strategy: We first reduce the size of x by noting that + + exp(kr + m * log(2)) = 2^m * exp(r)^k + + where m and k are integers. By choosing m appropriately + we can make |kr| <= log(2) / 2 = 0.347. Then exp(r) is + evaluated using the familiar Taylor series. Reducing the + argument substantially speeds up the convergence. */ + + const double k = ldexp(1.0, 16); + const double inv_k = 1.0 / k; + + if (a[0] <= -709.0) + return 0.0; + + if (a[0] >= 709.0) + return qd_real::_inf; + + if (a.is_zero()) + return 1.0; + + if (a.is_one()) + return qd_real::_e; + + double m = std::floor(a.x[0] / qd_real::_log2.x[0] + 0.5); + qd_real r = mul_pwr2(a - qd_real::_log2 * m, inv_k); + qd_real s, p, t; + double thresh = inv_k * qd_real::_eps; + + p = sqr(r); + s = r + mul_pwr2(p, 0.5); + int i = 0; + do { + p *= r; + t = p * inv_fact[i++]; + s += t; + } while (std::abs(to_double(t)) > thresh && i < 9); + + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s = mul_pwr2(s, 2.0) + sqr(s); + s += 1.0; + return ldexp(s, static_cast(m)); +} + +/* Logarithm. Computes log(x) in quad-double precision. + This is a natural logarithm (i.e., base e). */ +qd_real log(const qd_real &a) { + /* Strategy. The Taylor series for log converges much more + slowly than that of exp, due to the lack of the factorial + term in the denominator. Hence this routine instead tries + to determine the root of the function + + f(x) = exp(x) - a + + using Newton iteration. The iteration is given by + + x' = x - f(x)/f'(x) + = x - (1 - a * exp(-x)) + = x + a * exp(-x) - 1. + + Two iteration is needed, since Newton's iteration + approximately doubles the number of digits per iteration. */ + + if (a.is_one()) { + return 0.0; + } + + if (a[0] <= 0.0) { + qd_real::error("(qd_real::log): Non-positive argument."); + return qd_real::_nan; + } + + if (a[0] == 0.0) { + return -qd_real::_inf; + } + + qd_real x = std::log(a[0]); /* Initial approximation */ + + x = x + a * exp(-x) - 1.0; + x = x + a * exp(-x) - 1.0; + x = x + a * exp(-x) - 1.0; + + return x; +} + +qd_real log10(const qd_real &a) { + return log(a) / qd_real::_log10; +} + +static const qd_real _pi1024 = qd_real( + 3.067961575771282340e-03, 1.195944139792337116e-19, + -2.924579892303066080e-36, 1.086381075061880158e-52); + +/* Table of sin(k * pi/1024) and cos(k * pi/1024). */ +static const qd_real sin_table [] = { + qd_real( 3.0679567629659761e-03, 1.2690279085455925e-19, + 5.2879464245328389e-36, -1.7820334081955298e-52), + qd_real( 6.1358846491544753e-03, 9.0545257482474933e-20, + 1.6260113133745320e-37, -9.7492001208767410e-55), + qd_real( 9.2037547820598194e-03, -1.2136591693535934e-19, + 5.5696903949425567e-36, 1.2505635791936951e-52), + qd_real( 1.2271538285719925e-02, 6.9197907640283170e-19, + -4.0203726713435555e-36, -2.0688703606952816e-52), + qd_real( 1.5339206284988102e-02, -8.4462578865401696e-19, + 4.6535897505058629e-35, -1.3923682978570467e-51), + qd_real( 1.8406729905804820e-02, 7.4195533812833160e-19, + 3.9068476486787607e-35, 3.6393321292898614e-52), + qd_real( 2.1474080275469508e-02, -4.5407960207688566e-19, + -2.2031770119723005e-35, 1.2709814654833741e-51), + qd_real( 2.4541228522912288e-02, -9.1868490125778782e-20, + 4.8706148704467061e-36, -2.8153947855469224e-52), + qd_real( 2.7608145778965743e-02, -1.5932358831389269e-18, + -7.0475416242776030e-35, -2.7518494176602744e-51), + qd_real( 3.0674803176636626e-02, -1.6936054844107918e-20, + -2.0039543064442544e-36, -1.6267505108658196e-52), + qd_real( 3.3741171851377587e-02, -2.0096074292368340e-18, + -1.3548237016537134e-34, 6.5554881875899973e-51), + qd_real( 3.6807222941358832e-02, 6.1060088803529842e-19, + -4.0448721259852727e-35, -2.1111056765671495e-51), + qd_real( 3.9872927587739811e-02, 4.6657453481183289e-19, + 3.4119333562288684e-35, 2.4007534726187511e-51), + qd_real( 4.2938256934940820e-02, 2.8351940588660907e-18, + 1.6991309601186475e-34, 6.8026536098672629e-51), + qd_real( 4.6003182130914630e-02, -1.1182813940157788e-18, + 7.5235020270378946e-35, 4.1187304955493722e-52), + qd_real( 4.9067674327418015e-02, -6.7961037205182801e-19, + -4.4318868124718325e-35, -9.9376628132525316e-52), + qd_real( 5.2131704680283324e-02, -2.4243695291953779e-18, + -1.3675405320092298e-34, -8.3938137621145070e-51), + qd_real( 5.5195244349689941e-02, -1.3340299860891103e-18, + -3.4359574125665608e-35, 1.1911462755409369e-51), + qd_real( 5.8258264500435759e-02, 2.3299905496077492e-19, + 1.9376108990628660e-36, -5.1273775710095301e-53), + qd_real( 6.1320736302208578e-02, -5.1181134064638108e-19, + -4.2726335866706313e-35, 2.6368495557440691e-51), + qd_real( 6.4382630929857465e-02, -4.2325997000052705e-18, + 3.3260117711855937e-35, 1.4736267706718352e-51), + qd_real( 6.7443919563664065e-02, -6.9221796556983636e-18, + 1.5909286358911040e-34, -7.8828946891835218e-51), + qd_real( 7.0504573389613870e-02, -6.8552791107342883e-18, + -1.9961177630841580e-34, 2.0127129580485300e-50), + qd_real( 7.3564563599667426e-02, -2.7784941506273593e-18, + -9.1240375489852821e-35, -1.9589752023546795e-51), + qd_real( 7.6623861392031492e-02, 2.3253700287958801e-19, + -1.3186083921213440e-36, -4.9927872608099673e-53), + qd_real( 7.9682437971430126e-02, -4.4867664311373041e-18, + 2.8540789143650264e-34, 2.8491348583262741e-51), + qd_real( 8.2740264549375692e-02, 1.4735983530877760e-18, + 3.7284093452233713e-35, 2.9024430036724088e-52), + qd_real( 8.5797312344439894e-02, -3.3881893830684029e-18, + -1.6135529531508258e-34, 7.7294651620588049e-51), + qd_real( 8.8853552582524600e-02, -3.7501775830290691e-18, + 3.7543606373911573e-34, 2.2233701854451859e-50), + qd_real( 9.1908956497132724e-02, 4.7631594854274564e-18, + 1.5722874642939344e-34, -4.8464145447831456e-51), + qd_real( 9.4963495329639006e-02, -6.5885886400417564e-18, + -2.1371116991641965e-34, 1.3819370559249300e-50), + qd_real( 9.8017140329560604e-02, -1.6345823622442560e-18, + -1.3209238810006454e-35, -3.5691060049117942e-52), + qd_real( 1.0106986275482782e-01, 3.3164325719308656e-18, + -1.2004224885132282e-34, 7.2028828495418631e-51), + qd_real( 1.0412163387205457e-01, 6.5760254085385100e-18, + 1.7066246171219214e-34, -4.9499340996893514e-51), + qd_real( 1.0717242495680884e-01, 6.4424044279026198e-18, + -8.3956976499698139e-35, -4.0667730213318321e-51), + qd_real( 1.1022220729388306e-01, -5.6789503537823233e-19, + 1.0380274792383233e-35, 1.5213997918456695e-52), + qd_real( 1.1327095217756435e-01, 2.7100481012132900e-18, + 1.5323292999491619e-35, 4.9564432810360879e-52), + qd_real( 1.1631863091190477e-01, 1.0294914877509705e-18, + -9.3975734948993038e-35, 1.3534827323719708e-52), + qd_real( 1.1936521481099137e-01, -3.9500089391898506e-18, + 3.5317349978227311e-34, 1.8856046807012275e-51), + qd_real( 1.2241067519921620e-01, 2.8354501489965335e-18, + 1.8151655751493305e-34, -2.8716592177915192e-51), + qd_real( 1.2545498341154623e-01, 4.8686751763148235e-18, + 5.9878105258097936e-35, -3.3534629098722107e-51), + qd_real( 1.2849811079379317e-01, 3.8198603954988802e-18, + -1.8627501455947798e-34, -2.4308161133527791e-51), + qd_real( 1.3154002870288312e-01, -5.0039708262213813e-18, + -1.2983004159245552e-34, -4.6872034915794122e-51), + qd_real( 1.3458070850712620e-01, -9.1670359171480699e-18, + 1.5916493007073973e-34, 4.0237002484366833e-51), + qd_real( 1.3762012158648604e-01, 6.6253255866774482e-18, + -2.3746583031401459e-34, -9.3703876173093250e-52), 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2.7462312204277281e-17, + -4.6552847802111948e-34, 6.5961781099193122e-51), + qd_real( 4.2755509343028208e-01, 9.4111898162954726e-18, + -1.7446682426598801e-34, -2.2054492626480169e-51), + qd_real( 4.3032648134008261e-01, 2.2259686974092690e-17, + 8.5972591314085075e-34, -2.9420897889003020e-50), + qd_real( 4.3309381885315196e-01, 1.1224283329847517e-17, + 5.3223748041075651e-35, 5.3926192627014212e-51), + qd_real( 4.3585707992225547e-01, 1.6230515450644527e-17, + -6.4371449063579431e-35, -6.9102436481386757e-51), + qd_real( 4.3861623853852766e-01, -2.0883315831075090e-17, + -1.4259583540891877e-34, 6.3864763590657077e-52), + qd_real( 4.4137126873171667e-01, 2.2360783886964969e-17, + 1.1864769603515770e-34, -3.8087003266189232e-51), + qd_real( 4.4412214457042926e-01, -2.4218874422178315e-17, + 2.2205230838703907e-34, 9.2133035911356258e-51), + qd_real( 4.4686884016237421e-01, -1.9222136142309382e-17, + -4.4425678589732049e-35, -1.3673609292149535e-51), + qd_real( 4.4961132965460660e-01, 4.8831924232035243e-18, + 2.7151084498191381e-34, -1.5653993171613154e-50), + qd_real( 4.5234958723377089e-01, -1.4827977472196122e-17, + -7.6947501088972324e-34, 1.7656856882031319e-50), + qd_real( 4.5508358712634384e-01, -1.2379906758116472e-17, + 5.5289688955542643e-34, -8.5382312840209386e-51), + qd_real( 4.5781330359887723e-01, -8.4554254922295949e-18, + -6.3770394246764263e-34, 3.1778253575564249e-50), + qd_real( 4.6053871095824001e-01, 1.8488777492177872e-17, + -1.0527732154209725e-33, 3.3235593490947102e-50), + qd_real( 4.6325978355186020e-01, -7.3514924533231707e-18, + 6.7175396881707035e-34, 3.9594127612123379e-50), + qd_real( 4.6597649576796618e-01, -3.3023547778235135e-18, + 3.4904677050476886e-35, 3.4483855263874246e-51), + qd_real( 4.6868882203582796e-01, -2.2949251681845054e-17, + -1.1364757641823658e-33, 6.8840522501918612e-50), + qd_real( 4.7139673682599764e-01, 6.5166781360690130e-18, + 2.9457546966235984e-34, -6.2159717738836630e-51), + qd_real( 4.7410021465055002e-01, -8.1451601548978075e-18, + -3.4789448555614422e-34, -1.1681943974658508e-50), + qd_real( 4.7679923006332214e-01, -1.0293515338305794e-17, + -3.6582045008369952e-34, 1.7424131479176475e-50), + qd_real( 4.7949375766015301e-01, 1.8419999662684771e-17, + -1.3040838621273312e-33, 1.0977131822246471e-50), + qd_real( 4.8218377207912277e-01, -2.5861500925520442e-17, + -6.2913197606500007e-36, 4.0802359808684726e-52), + qd_real( 4.8486924800079112e-01, -1.8034004203262245e-17, + -3.5244276906958044e-34, -1.7138318654749246e-50), + qd_real( 4.8755016014843594e-01, 1.4231090931273653e-17, + -1.8277733073262697e-34, -1.5208291790429557e-51), + qd_real( 4.9022648328829116e-01, -5.1496145643440404e-18, + -3.6903027405284104e-34, 1.5172940095151304e-50), + qd_real( 4.9289819222978404e-01, -1.0257831676562186e-18, + 6.9520817760885069e-35, -2.4260961214090389e-51), + qd_real( 4.9556526182577254e-01, -9.4323241942365362e-18, + 3.1212918657699143e-35, 4.2009072375242736e-52), + qd_real( 4.9822766697278187e-01, -1.6126383830540798e-17, + -1.5092897319298871e-33, 1.1049298890895917e-50), + qd_real( 5.0088538261124083e-01, -3.9604015147074639e-17, + -2.2208395201898007e-33, 1.3648202735839417e-49), + qd_real( 5.0353838372571758e-01, -1.6731308204967497e-17, + -1.0140233644074786e-33, 4.0953071937671477e-50), + qd_real( 5.0618664534515534e-01, -4.8321592986493711e-17, + 9.2858107226642252e-34, 4.2699802401037005e-50), + qd_real( 5.0883014254310699e-01, 4.7836968268014130e-17, + -1.0727022928806035e-33, 2.7309374513672757e-50), + qd_real( 5.1146885043797041e-01, -1.3088001221007579e-17, + 4.0929033363366899e-34, -3.7952190153477926e-50), + qd_real( 5.1410274419322177e-01, -4.5712707523615624e-17, + 1.5488279442238283e-33, -2.5853959305521130e-50), + qd_real( 5.1673179901764987e-01, 8.3018617233836515e-18, + 5.8251027467695202e-34, -2.2812397190535076e-50), + qd_real( 5.1935599016558964e-01, -5.5331248144171145e-17, + -3.1628375609769026e-35, -2.4091972051188571e-51), + qd_real( 5.2197529293715439e-01, -4.6555795692088883e-17, + 4.6378980936850430e-34, -3.3470542934689532e-51), + qd_real( 5.2458968267846895e-01, -4.3068869040082345e-17, + -4.2013155291932055e-34, -1.5096069926700274e-50), + qd_real( 5.2719913478190139e-01, -4.2202983480560619e-17, + 8.5585916184867295e-34, 7.9974339336732307e-50), + qd_real( 5.2980362468629472e-01, -4.8067841706482342e-17, + 5.8309721046630296e-34, -8.9740761521756660e-51), + qd_real( 5.3240312787719801e-01, -4.1020306135800895e-17, + -1.9239996374230821e-33, -1.5326987913812184e-49), + qd_real( 5.3499761988709726e-01, -5.3683132708358134e-17, + -1.3900569918838112e-33, 2.7154084726474092e-50), + qd_real( 5.3758707629564551e-01, -2.2617365388403054e-17, + -5.9787279033447075e-34, 3.1204419729043625e-51), + qd_real( 5.4017147272989285e-01, 2.7072447965935839e-17, + 1.1698799709213829e-33, -5.9094668515881500e-50), + qd_real( 5.4275078486451589e-01, 1.7148261004757101e-17, + -1.3525905925200870e-33, 4.9724411290727323e-50), + qd_real( 5.4532498842204646e-01, -4.1517817538384258e-17, + -1.5318930219385941e-33, 6.3629921101413974e-50), + qd_real( 5.4789405917310019e-01, -2.4065878297113363e-17, + -3.5639213669362606e-36, -2.6013270854271645e-52), + qd_real( 5.5045797293660481e-01, -8.3319903015807663e-18, + -2.3058454035767633e-34, -2.1611290432369010e-50), + qd_real( 5.5301670558002758e-01, -4.7061536623798204e-17, + -1.0617111545918056e-33, -1.6196316144407379e-50), + qd_real( 5.5557023301960218e-01, 4.7094109405616768e-17, + -2.0640520383682921e-33, 1.2290163188567138e-49), + qd_real( 5.5811853122055610e-01, 1.3481176324765226e-17, + -5.5016743873011438e-34, -2.3484822739335416e-50), + qd_real( 5.6066157619733603e-01, -7.3956418153476152e-18, + 3.9680620611731193e-34, 3.1995952200836223e-50), + qd_real( 5.6319934401383409e-01, 2.3835775146854829e-17, + 1.3511793173769814e-34, 9.3201311581248143e-51), + qd_real( 5.6573181078361323e-01, -3.4096079596590466e-17, + -1.7073289744303546e-33, 8.9147089975404507e-50), + qd_real( 5.6825895267013160e-01, -5.0935673642769248e-17, + -1.6274356351028249e-33, 9.8183151561702966e-51), + qd_real( 5.7078074588696726e-01, 2.4568151455566208e-17, + -1.2844481247560350e-33, -1.8037634376936261e-50), + qd_real( 5.7329716669804220e-01, 8.5176611669306400e-18, + -6.4443208788026766e-34, 2.2546105543273003e-50), + qd_real( 5.7580819141784534e-01, -3.7909495458942734e-17, + -2.7433738046854309e-33, 1.1130841524216795e-49), + qd_real( 5.7831379641165559e-01, -2.6237691512372831e-17, + 1.3679051680738167e-33, -3.1409808935335900e-50), + qd_real( 5.8081395809576453e-01, 1.8585338586613408e-17, + 2.7673843114549181e-34, 1.9605349619836937e-50), + qd_real( 5.8330865293769829e-01, 3.4516601079044858e-18, + 1.8065977478946306e-34, -6.3953958038544646e-51), + qd_real( 5.8579785745643886e-01, -3.7485501964311294e-18, + 2.7965403775536614e-34, -7.1816936024157202e-51), + qd_real( 5.8828154822264533e-01, -2.9292166725006846e-17, + -2.3744954603693934e-33, -1.1571631191512480e-50), + qd_real( 5.9075970185887428e-01, -4.7013584170659542e-17, + 2.4808417611768356e-33, 1.2598907673643198e-50), + qd_real( 5.9323229503979980e-01, 1.2892320944189053e-17, + 5.3058364776359583e-34, 4.1141674699390052e-50), + qd_real( 5.9569930449243336e-01, -1.3438641936579467e-17, + -6.7877687907721049e-35, -5.6046937531684890e-51), + qd_real( 5.9816070699634227e-01, 3.8801885783000657e-17, + -1.2084165858094663e-33, -4.0456610843430061e-50), + qd_real( 6.0061647938386897e-01, -4.6398198229461932e-17, + -1.6673493003710801e-33, 5.1982824378491445e-50), + qd_real( 6.0306659854034816e-01, 3.7323357680559650e-17, + 2.7771920866974305e-33, -1.6194229649742458e-49), + qd_real( 6.0551104140432555e-01, -3.1202672493305677e-17, + 1.2761267338680916e-33, -4.0859368598379647e-50), + qd_real( 6.0794978496777363e-01, 3.5160832362096660e-17, + -2.5546242776778394e-34, -1.4085313551220694e-50), + qd_real( 6.1038280627630948e-01, -2.2563265648229169e-17, + 1.3185575011226730e-33, 8.2316691420063460e-50), + qd_real( 6.1281008242940971e-01, -4.2693476568409685e-18, + 2.5839965886650320e-34, 1.6884412005622537e-50), + qd_real( 6.1523159058062682e-01, 2.6231417767266950e-17, + -1.4095366621106716e-33, 7.2058690491304558e-50), + qd_real( 6.1764730793780398e-01, -4.7478594510902452e-17, + -7.2986558263123996e-34, -3.0152327517439154e-50), + qd_real( 6.2005721176328921e-01, -2.7983410837681118e-17, + 1.1649951056138923e-33, -5.4539089117135207e-50), + qd_real( 6.2246127937414997e-01, 5.2940728606573002e-18, + -4.8486411215945827e-35, 1.2696527641980109e-52), + qd_real( 6.2485948814238634e-01, 3.3671846037243900e-17, + -2.7846053391012096e-33, 5.6102718120012104e-50), + qd_real( 6.2725181549514408e-01, 3.0763585181253225e-17, + 2.7068930273498138e-34, -1.1172240309286484e-50), + qd_real( 6.2963823891492698e-01, 4.1115334049626806e-17, + -1.9167473580230747e-33, 1.1118424028161730e-49), + qd_real( 6.3201873593980906e-01, -4.0164942296463612e-17, + -7.2208643641736723e-34, 3.7828920470544344e-50), + qd_real( 6.3439328416364549e-01, 1.0420901929280035e-17, + 4.1174558929280492e-34, -1.4464152986630705e-51), + qd_real( 6.3676186123628420e-01, 3.1419048711901611e-17, + -2.2693738415126449e-33, -1.6023584204297388e-49), + qd_real( 6.3912444486377573e-01, 1.2416796312271043e-17, + -6.2095419626356605e-34, 2.7762065999506603e-50), + qd_real( 6.4148101280858316e-01, -9.9883430115943310e-18, + 4.1969230376730128e-34, 5.6980543799257597e-51), + qd_real( 6.4383154288979150e-01, -3.2084798795046886e-17, + -1.2595311907053305e-33, -4.0205885230841536e-50), + qd_real( 6.4617601298331639e-01, -2.9756137382280815e-17, + -1.0275370077518259e-33, 8.0852478665893014e-51), + qd_real( 6.4851440102211244e-01, 3.9870270313386831e-18, + 1.9408388509540788e-34, -5.1798420636193190e-51), + qd_real( 6.5084668499638088e-01, 3.9714670710500257e-17, + 2.9178546787002963e-34, 3.8140635508293278e-51), + qd_real( 6.5317284295377676e-01, 8.5695642060026238e-18, + -6.9165322305070633e-34, 2.3873751224185395e-50), + qd_real( 6.5549285299961535e-01, 3.5638734426385005e-17, + 1.2695365790889811e-33, 4.3984952865412050e-50), + qd_real( 6.5780669329707864e-01, 1.9580943058468545e-17, + -1.1944272256627192e-33, 2.8556402616436858e-50), + qd_real( 6.6011434206742048e-01, -1.3960054386823638e-19, + 6.1515777931494047e-36, 5.3510498875622660e-52), + qd_real( 6.6241577759017178e-01, -2.2615508885764591e-17, + 5.0177050318126862e-34, 2.9162532399530762e-50), + qd_real( 6.6471097820334490e-01, -3.6227793598034367e-17, + -9.0607934765540427e-34, 3.0917036342380213e-50), + qd_real( 6.6699992230363747e-01, 3.5284364997428166e-17, + -1.0382057232458238e-33, 7.3812756550167626e-50), + qd_real( 6.6928258834663612e-01, -5.4592652417447913e-17, + -2.5181014709695152e-33, -1.6867875999437174e-49), + qd_real( 6.7155895484701844e-01, -4.0489037749296692e-17, + 3.1995835625355681e-34, -1.4044414655670960e-50), + qd_real( 6.7382900037875604e-01, 2.3091901236161086e-17, + 5.7428037192881319e-34, 1.1240668354625977e-50), + qd_real( 6.7609270357531592e-01, 3.7256902248049466e-17, + 1.7059417895764375e-33, 9.7326347795300652e-50), + qd_real( 6.7835004312986147e-01, 1.8302093041863122e-17, + 9.5241675746813072e-34, 5.0328101116133503e-50), + qd_real( 6.8060099779545302e-01, 2.8473293354522047e-17, + 4.1331805977270903e-34, 4.2579030510748576e-50), + qd_real( 6.8284554638524808e-01, -1.2958058061524531e-17, + 1.8292386959330698e-34, 3.4536209116044487e-51), + qd_real( 6.8508366777270036e-01, 2.5948135194645137e-17, + -8.5030743129500702e-34, -6.9572086141009930e-50), + qd_real( 6.8731534089175916e-01, -5.5156158714917168e-17, + 1.1896489854266829e-33, -7.8505896218220662e-51), + qd_real( 6.8954054473706694e-01, -1.5889323294806790e-17, + 9.1242356240205712e-34, 3.8315454152267638e-50), + qd_real( 6.9175925836415775e-01, 2.7406078472410668e-17, + 1.3286508943202092e-33, 1.0651869129580079e-51), + qd_real( 6.9397146088965400e-01, 7.4345076956280137e-18, + 7.5061528388197460e-34, -1.5928000240686583e-50), + qd_real( 6.9617713149146299e-01, -4.1224081213582889e-17, + -3.1838716762083291e-35, -3.9625587412119131e-51), + qd_real( 6.9837624940897280e-01, 4.8988282435667768e-17, + 1.9134010413244152e-33, 2.6161153243793989e-50), + qd_real( 7.0056879394324834e-01, 3.1027960192992922e-17, + 9.5638250509179997e-34, 4.5896916138107048e-51), + qd_real( 7.0275474445722530e-01, 2.5278294383629822e-18, + -8.6985561210674942e-35, -5.6899862307812990e-51), + qd_real( 7.0493408037590488e-01, 2.7608725585748502e-17, + 2.9816599471629137e-34, 1.1533044185111206e-50), + qd_real( 7.0710678118654757e-01, -4.8336466567264567e-17, + 2.0693376543497068e-33, 2.4677734957341755e-50) +}; + +static const qd_real cos_table [] = { + qd_real( 9.9999529380957619e-01, -1.9668064285322189e-17, + -6.3053955095883481e-34, 5.3266110855726731e-52), + qd_real( 9.9998117528260111e-01, 3.3568103522895585e-17, + -1.4740132559368063e-35, 9.8603097594755596e-52), + qd_real( 9.9995764455196390e-01, -3.1527836866647287e-17, + 2.6363251186638437e-33, 1.0007504815488399e-49), + qd_real( 9.9992470183914450e-01, 3.7931082512668012e-17, + -8.5099918660501484e-35, -4.9956973223295153e-51), + qd_real( 9.9988234745421256e-01, -3.5477814872408538e-17, + 1.7102001035303974e-33, -1.0725388519026542e-49), + qd_real( 9.9983058179582340e-01, 1.8825140517551119e-17, + -5.1383513457616937e-34, -3.8378827995403787e-50), + qd_real( 9.9976940535121528e-01, 4.2681177032289012e-17, + 1.9062302359737099e-33, -6.0221153262881160e-50), + qd_real( 9.9969881869620425e-01, -2.9851486403799753e-17, + -1.9084787370733737e-33, 5.5980260344029202e-51), + qd_real( 9.9961882249517864e-01, -4.1181965521424734e-17, + 2.0915365593699916e-33, 8.1403390920903734e-50), + qd_real( 9.9952941750109314e-01, 2.0517917823755591e-17, + -4.7673802585706520e-34, -2.9443604198656772e-50), + qd_real( 9.9943060455546173e-01, 3.9644497752257798e-17, + -2.3757223716722428e-34, -1.2856759011361726e-51), + qd_real( 9.9932238458834954e-01, -4.2858538440845682e-17, + 3.3235101605146565e-34, -8.3554272377057543e-51), + qd_real( 9.9920475861836389e-01, 9.1796317110385693e-18, + 5.5416208185868570e-34, 8.0267046717615311e-52), + qd_real( 9.9907772775264536e-01, 2.1419007653587032e-17, + -7.9048203318529618e-34, -5.3166296181112712e-50), + qd_real( 9.9894129318685687e-01, -2.0610641910058638e-17, + -1.2546525485913485e-33, -7.5175888806157064e-50), + qd_real( 9.9879545620517241e-01, -1.2291693337075465e-17, + 2.4468446786491271e-34, 1.0723891085210268e-50), + qd_real( 9.9864021818026527e-01, -4.8690254312923302e-17, + -2.9470881967909147e-34, -1.3000650761346907e-50), + qd_real( 9.9847558057329477e-01, -2.2002931182778795e-17, + -1.2371509454944992e-33, -2.4911225131232065e-50), + qd_real( 9.9830154493389289e-01, 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2.9720241208332759e-34, -1.2521388928220163e-50), + qd_real( 7.5720884650648457e-01, -1.9909098777335502e-17, + 3.9409283266158482e-34, 2.0744254207802976e-50), + qd_real( 7.5520137689653655e-01, -1.9402238001823017e-17, + -3.7756206444727573e-34, -2.1212242308178287e-50), + qd_real( 7.5318679904361252e-01, -3.7937789838736540e-17, + -6.7009539920231559e-34, -6.7128562115050214e-51), + qd_real( 7.5116513190968637e-01, 4.3499761158645868e-17, + 2.5227718971102212e-33, -6.5969709212757102e-50), + qd_real( 7.4913639452345937e-01, -4.4729078447011889e-17, + -2.4206025249983768e-33, 1.1336681351116422e-49), + qd_real( 7.4710060598018013e-01, 1.1874824875965430e-17, + 2.1992523849833518e-34, 1.1025018564644483e-50), + qd_real( 7.4505778544146595e-01, 1.5078686911877863e-17, + 8.0898987212942471e-34, 8.2677958765323532e-50), + qd_real( 7.4300795213512172e-01, -2.5144629669719265e-17, + 7.1128989512526157e-34, 3.0181629077821220e-50), + qd_real( 7.4095112535495911e-01, -1.4708616952297345e-17, 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-2.3871262053452047e-17, + 1.0636125432862273e-33, -4.4598638837802517e-50), + qd_real( 7.2000250796138165e-01, -2.5689658854462333e-17, + -9.1492566948567925e-34, 4.4403780801267786e-50), + qd_real( 7.1787004505573171e-01, 2.7006476062511453e-17, + -2.2854956580215348e-34, 9.1726903890287867e-51), + qd_real( 7.1573082528381871e-01, -5.1581018476410262e-17, + -1.3736271349300259e-34, -1.2734611344111297e-50), + qd_real( 7.1358486878079364e-01, -4.2342504403133584e-17, + -4.2690366101617268e-34, -2.6352370883066522e-50), + qd_real( 7.1143219574521643e-01, 7.9643298613856813e-18, + 2.9488239510721469e-34, 1.6985236437666356e-50), + qd_real( 7.0927282643886569e-01, -3.7597359110245730e-17, + 1.0613125954645119e-34, 8.9465480185486032e-51), + qd_real( 7.0710678118654757e-01, -4.8336466567264567e-17, + 2.0693376543497068e-33, 2.4677734957341755e-50) +}; + +/* Computes sin(a) and cos(a) using Taylor series. + Assumes |a| <= pi/2048. */ +static void sincos_taylor(const qd_real &a, + qd_real &sin_a, qd_real &cos_a) { + const double thresh = 0.5 * qd_real::_eps * std::abs(to_double(a)); + qd_real p, s, t, x; + + if (a.is_zero()) { + sin_a = 0.0; + cos_a = 1.0; + return; + } + + x = -sqr(a); + s = a; + p = a; + int i = 0; + do { + p *= x; + t = p * inv_fact[i]; + s += t; + i += 2; + } while (i < n_inv_fact && std::abs(to_double(t)) > thresh); + + sin_a = s; + cos_a = sqrt(1.0 - sqr(s)); +} + +static qd_real sin_taylor(const qd_real &a) { + const double thresh = 0.5 * qd_real::_eps * std::abs(to_double(a)); + qd_real p, s, t, x; + + if (a.is_zero()) { + return 0.0; + } + + x = -sqr(a); + s = a; + p = a; + int i = 0; + do { + p *= x; + t = p * inv_fact[i]; + s += t; + i += 2; + } while (i < n_inv_fact && std::abs(to_double(t)) > thresh); + + return s; +} + +static qd_real cos_taylor(const qd_real &a) { + const double thresh = 0.5 * qd_real::_eps; + qd_real p, s, t, x; + + if (a.is_zero()) { + return 1.0; + } + + x = -sqr(a); + s = 1.0 + mul_pwr2(x, 0.5); + p = x; + int i = 1; + do { + p *= x; + t = p * inv_fact[i]; + s += t; + i += 2; + } while (i < n_inv_fact && std::abs(to_double(t)) > thresh); + + return s; +} + +qd_real sin(const qd_real &a) { + + /* Strategy. To compute sin(x), we choose integers a, b so that + + x = s + a * (pi/2) + b * (pi/1024) + + and |s| <= pi/2048. Using a precomputed table of + sin(k pi / 1024) and cos(k pi / 1024), we can compute + sin(x) from sin(s) and cos(s). This greatly increases the + convergence of the sine Taylor series. */ + + if (a.is_zero()) { + return 0.0; + } + + // approximately reduce modulo 2*pi + qd_real z = nint(a / qd_real::_2pi); + qd_real r = a - qd_real::_2pi * z; + + // approximately reduce modulo pi/2 and then modulo pi/1024 + double q = std::floor(r.x[0] / qd_real::_pi2[0] + 0.5); + qd_real t = r - qd_real::_pi2 * q; + int j = static_cast(q); + q = std::floor(t.x[0] / _pi1024[0] + 0.5); + t -= _pi1024 * q; + int k = static_cast(q); + int abs_k = std::abs(k); + + if (j < -2 || j > 2) { + qd_real::error("(qd_real::sin): Cannot reduce modulo pi/2."); + return qd_real::_nan; + } + + if (abs_k > 256) { + qd_real::error("(qd_real::sin): Cannot reduce modulo pi/1024."); + return qd_real::_nan; + } + + if (k == 0) { + switch (j) { + case 0: + return sin_taylor(t); + case 1: + return cos_taylor(t); + case -1: + return -cos_taylor(t); + default: + return -sin_taylor(t); + } + } + + qd_real sin_t, cos_t; + qd_real u = cos_table[abs_k-1]; + qd_real v = sin_table[abs_k-1]; + sincos_taylor(t, sin_t, cos_t); + + if (j == 0) { + if (k > 0) { + r = u * sin_t + v * cos_t; + } else { + r = u * sin_t - v * cos_t; + } + } else if (j == 1) { + if (k > 0) { + r = u * cos_t - v * sin_t; + } else { + r = u * cos_t + v * sin_t; + } + } else if (j == -1) { + if (k > 0) { + r = v * sin_t - u * cos_t; + } else { + r = - u * cos_t - v * sin_t; + } + } else { + if (k > 0) { + r = - u * sin_t - v * cos_t; + } else { + r = v * cos_t - u * sin_t; + } + } + + return r; +} + +qd_real cos(const qd_real &a) { + + if (a.is_zero()) { + return 1.0; + } + + // approximately reduce modulo 2*pi + qd_real z = nint(a / qd_real::_2pi); + qd_real r = a - qd_real::_2pi * z; + + // approximately reduce modulo pi/2 and then modulo pi/1024 + double q = std::floor(r.x[0] / qd_real::_pi2.x[0] + 0.5); + qd_real t = r - qd_real::_pi2 * q; + int j = static_cast(q); + q = std::floor(t.x[0] / _pi1024.x[0] + 0.5); + t -= _pi1024 * q; + int k = static_cast(q); + int abs_k = std::abs(k); + + if (j < -2 || j > 2) { + qd_real::error("(qd_real::cos): Cannot reduce modulo pi/2."); + return qd_real::_nan; + } + + if (abs_k > 256) { + qd_real::error("(qd_real::cos): Cannot reduce modulo pi/1024."); + return qd_real::_nan; + } + + if (k == 0) { + switch (j) { + case 0: + return cos_taylor(t); + case 1: + return -sin_taylor(t); + case -1: + return sin_taylor(t); + default: + return -cos_taylor(t); + } + } + + qd_real sin_t, cos_t; + sincos_taylor(t, sin_t, cos_t); + + qd_real u = cos_table[abs_k-1]; + qd_real v = sin_table[abs_k-1]; + + if (j == 0) { + if (k > 0) { + r = u * cos_t - v * sin_t; + } else { + r = u * cos_t + v * sin_t; + } + } else if (j == 1) { + if (k > 0) { + r = - u * sin_t - v * cos_t; + } else { + r = v * cos_t - u * sin_t; + } + } else if (j == -1) { + if (k > 0) { + r = u * sin_t + v * cos_t; + } else { + r = u * sin_t - v * cos_t; + } + } else { + if (k > 0) { + r = v * sin_t - u * cos_t; + } else { + r = - u * cos_t - v * sin_t; + } + } + + return r; +} + +void sincos(const qd_real &a, qd_real &sin_a, qd_real &cos_a) { + + if (a.is_zero()) { + sin_a = 0.0; + cos_a = 1.0; + return; + } + + // approximately reduce by 2*pi + qd_real z = nint(a / qd_real::_2pi); + qd_real t = a - qd_real::_2pi * z; + + // approximately reduce by pi/2 and then by pi/1024. + double q = std::floor(t.x[0] / qd_real::_pi2.x[0] + 0.5); + t -= qd_real::_pi2 * q; + int j = static_cast(q); + q = std::floor(t.x[0] / _pi1024.x[0] + 0.5); + t -= _pi1024 * q; + int k = static_cast(q); + int abs_k = std::abs(k); + + if (j < -2 || j > 2) { + qd_real::error("(qd_real::sincos): Cannot reduce modulo pi/2."); + cos_a = sin_a = qd_real::_nan; + return; + } + + if (abs_k > 256) { + qd_real::error("(qd_real::sincos): Cannot reduce modulo pi/1024."); + cos_a = sin_a = qd_real::_nan; + return; + } + + qd_real sin_t, cos_t; + sincos_taylor(t, sin_t, cos_t); + + if (k == 0) { + if (j == 0) { + sin_a = sin_t; + cos_a = cos_t; + } else if (j == 1) { + sin_a = cos_t; + cos_a = -sin_t; + } else if (j == -1) { + sin_a = -cos_t; + cos_a = sin_t; + } else { + sin_a = -sin_t; + cos_a = -cos_t; + } + return; + } + + qd_real u = cos_table[abs_k-1]; + qd_real v = sin_table[abs_k-1]; + + if (j == 0) { + if (k > 0) { + sin_a = u * sin_t + v * cos_t; + cos_a = u * cos_t - v * sin_t; + } else { + sin_a = u * sin_t - v * cos_t; + cos_a = u * cos_t + v * sin_t; + } + } else if (j == 1) { + if (k > 0) { + cos_a = - u * sin_t - v * cos_t; + sin_a = u * cos_t - v * sin_t; + } else { + cos_a = v * cos_t - u * sin_t; + sin_a = u * cos_t + v * sin_t; + } + } else if (j == -1) { + if (k > 0) { + cos_a = u * sin_t + v * cos_t; + sin_a = v * sin_t - u * cos_t; + } else { + cos_a = u * sin_t - v * cos_t; + sin_a = - u * cos_t - v * sin_t; + } + } else { + if (k > 0) { + sin_a = - u * sin_t - v * cos_t; + cos_a = v * sin_t - u * cos_t; + } else { + sin_a = v * cos_t - u * sin_t; + cos_a = - u * cos_t - v * sin_t; + } + } +} + +qd_real atan(const qd_real &a) { + return atan2(a, qd_real(1.0)); +} + +qd_real atan2(const qd_real &y, const qd_real &x) { + /* Strategy: Instead of using Taylor series to compute + arctan, we instead use Newton's iteration to solve + the equation + + sin(z) = y/r or cos(z) = x/r + + where r = sqrt(x^2 + y^2). + The iteration is given by + + z' = z + (y - sin(z)) / cos(z) (for equation 1) + z' = z - (x - cos(z)) / sin(z) (for equation 2) + + Here, x and y are normalized so that x^2 + y^2 = 1. + If |x| > |y|, then first iteration is used since the + denominator is larger. Otherwise, the second is used. + */ + + if (x.is_zero()) { + + if (y.is_zero()) { + /* Both x and y is zero. */ + qd_real::error("(qd_real::atan2): Both arguments zero."); + return qd_real::_nan; + } + + return (y.is_positive()) ? qd_real::_pi2 : -qd_real::_pi2; + } else if (y.is_zero()) { + return (x.is_positive()) ? qd_real(0.0) : qd_real::_pi; + } + + if (x == y) { + return (y.is_positive()) ? qd_real::_pi4 : -qd_real::_3pi4; + } + + if (x == -y) { + return (y.is_positive()) ? qd_real::_3pi4 : -qd_real::_pi4; + } + + qd_real r = sqrt(sqr(x) + sqr(y)); + qd_real xx = x / r; + qd_real yy = y / r; + + /* Compute double precision approximation to atan. */ + qd_real z = std::atan2(to_double(y), to_double(x)); + qd_real sin_z, cos_z; + + if (std::abs(xx.x[0]) > std::abs(yy.x[0])) { + /* Use Newton iteration 1. z' = z + (y - sin(z)) / cos(z) */ + sincos(z, sin_z, cos_z); + z += (yy - sin_z) / cos_z; + sincos(z, sin_z, cos_z); + z += (yy - sin_z) / cos_z; + sincos(z, sin_z, cos_z); + z += (yy - sin_z) / cos_z; + } else { + /* Use Newton iteration 2. z' = z - (x - cos(z)) / sin(z) */ + sincos(z, sin_z, cos_z); + z -= (xx - cos_z) / sin_z; + sincos(z, sin_z, cos_z); + z -= (xx - cos_z) / sin_z; + sincos(z, sin_z, cos_z); + z -= (xx - cos_z) / sin_z; + } + + return z; +} + + +qd_real drem(const qd_real &a, const qd_real &b) { + qd_real n = nint(a/b); + return (a - n * b); +} + +qd_real divrem(const qd_real &a, const qd_real &b, qd_real &r) { + qd_real n = nint(a/b); + r = a - n * b; + return n; +} + +qd_real tan(const qd_real &a) { + qd_real s, c; + sincos(a, s, c); + return s/c; +} + +qd_real asin(const qd_real &a) { + qd_real abs_a = abs(a); + + if (abs_a > 1.0) { + qd_real::error("(qd_real::asin): Argument out of domain."); + return qd_real::_nan; + } + + if (abs_a.is_one()) { + return (a.is_positive()) ? qd_real::_pi2 : -qd_real::_pi2; + } + + return atan2(a, sqrt(1.0 - sqr(a))); +} + +qd_real acos(const qd_real &a) { + qd_real abs_a = abs(a); + + if (abs_a > 1.0) { + qd_real::error("(qd_real::acos): Argument out of domain."); + return qd_real::_nan; + } + + if (abs_a.is_one()) { + return (a.is_positive()) ? qd_real(0.0) : qd_real::_pi; + } + + return atan2(sqrt(1.0 - sqr(a)), a); +} + +qd_real sinh(const qd_real &a) { + if (a.is_zero()) { + return 0.0; + } + + if (abs(a) > 0.05) { + qd_real ea = exp(a); + return mul_pwr2(ea - inv(ea), 0.5); + } + + /* Since a is small, using the above formula gives + a lot of cancellation. So use Taylor series. */ + qd_real s = a; + qd_real t = a; + qd_real r = sqr(t); + double m = 1.0; + double thresh = std::abs(to_double(a) * qd_real::_eps); + + do { + m += 2.0; + t *= r; + t /= (m-1) * m; + + s += t; + } while (abs(t) > thresh); + + return s; +} + +qd_real cosh(const qd_real &a) { + if (a.is_zero()) { + return 1.0; + } + + qd_real ea = exp(a); + return mul_pwr2(ea + inv(ea), 0.5); +} + +qd_real tanh(const qd_real &a) { + if (a.is_zero()) { + return 0.0; + } + + if (std::abs(to_double(a)) > 0.05) { + qd_real ea = exp(a); + qd_real inv_ea = inv(ea); + return (ea - inv_ea) / (ea + inv_ea); + } else { + qd_real s, c; + s = sinh(a); + c = sqrt(1.0 + sqr(s)); + return s / c; + } +} + +void sincosh(const qd_real &a, qd_real &s, qd_real &c) { + if (std::abs(to_double(a)) <= 0.05) { + s = sinh(a); + c = sqrt(1.0 + sqr(s)); + } else { + qd_real ea = exp(a); + qd_real inv_ea = inv(ea); + s = mul_pwr2(ea - inv_ea, 0.5); + c = mul_pwr2(ea + inv_ea, 0.5); + } +} + +qd_real asinh(const qd_real &a) { + return log(a + sqrt(sqr(a) + 1.0)); +} + +qd_real acosh(const qd_real &a) { + if (a < 1.0) { + qd_real::error("(qd_real::acosh): Argument out of domain."); + return qd_real::_nan; + } + + return log(a + sqrt(sqr(a) - 1.0)); +} + +qd_real atanh(const qd_real &a) { + if (abs(a) >= 1.0) { + qd_real::error("(qd_real::atanh): Argument out of domain."); + return qd_real::_nan; + } + + return mul_pwr2(log((1.0 + a) / (1.0 - a)), 0.5); +} + +QD_API qd_real fmod(const qd_real &a, const qd_real &b) { + qd_real n = aint(a / b); + return (a - b * n); +} + +QD_API qd_real qdrand() { + static const double m_const = 4.6566128730773926e-10; /* = 2^{-31} */ + double m = m_const; + qd_real r = 0.0; + double d; + + /* Strategy: Generate 31 bits at a time, using lrand48 + random number generator. Shift the bits, and repeat + 7 times. */ + + for (int i = 0; i < 7; i++, m *= m_const) { + d = std::rand() * m; + r += d; + } + + return r; +} + + +/* polyeval(c, n, x) + Evaluates the given n-th degree polynomial at x. + The polynomial is given by the array of (n+1) coefficients. */ +qd_real polyeval(const qd_real *c, int n, const qd_real &x) { + /* Just use Horner's method of polynomial evaluation. */ + qd_real r = c[n]; + + for (int i = n-1; i >= 0; i--) { + r *= x; + r += c[i]; + } + + return r; +} + +/* polyroot(c, n, x0) + Given an n-th degree polynomial, finds a root close to + the given guess x0. Note that this uses simple Newton + iteration scheme, and does not work for multiple roots. */ +QD_API qd_real polyroot(const qd_real *c, int n, + const qd_real &x0, int max_iter, double thresh) { + qd_real x = x0; + qd_real f; + qd_real *d = new qd_real[n]; + bool conv = false; + int i; + double max_c = std::abs(to_double(c[0])); + double v; + + if (thresh == 0.0) thresh = qd_real::_eps; + + /* Compute the coefficients of the derivatives. */ + for (i = 1; i <= n; i++) { + v = std::abs(to_double(c[i])); + if (v > max_c) max_c = v; + d[i-1] = c[i] * static_cast(i); + } + thresh *= max_c; + + /* Newton iteration. */ + for (i = 0; i < max_iter; i++) { + f = polyeval(c, n, x); + + if (abs(f) < thresh) { + conv = true; + break; + } + x -= (f / polyeval(d, n-1, x)); + } + delete [] d; + + if (!conv) { + qd_real::error("(qd_real::polyroot): Failed to converge."); + return qd_real::_nan; + } + + return x; +} + +qd_real qd_real::debug_rand() { + if (std::rand() % 2 == 0) + return qdrand(); + + int expn = 0; + qd_real a = 0.0; + double d; + for (int i = 0; i < 4; i++) { + d = std::ldexp(std::rand() / static_cast(RAND_MAX), -expn); + a += d; + expn = expn + 54 + std::rand() % 200; + } + return a; +} + diff --git a/external/PackedCSparse/qd/qd_real.h b/external/PackedCSparse/qd/qd_real.h new file mode 100644 index 000000000..1ecfc732c --- /dev/null +++ b/external/PackedCSparse/qd/qd_real.h @@ -0,0 +1,296 @@ +/* + * include/qd_real.h + * + * This work was supported by the Director, Office of Science, Division + * of Mathematical, Information, and Computational Sciences of the + * U.S. Department of Energy under contract number DE-AC03-76SF00098. + * + * Copyright (c) 2000-2007 + * + * Quad-double precision (>= 212-bit significand) floating point arithmetic + * package, written in ANSI C++, taking full advantage of operator overloading. + * Uses similar techniques as that of David Bailey's double-double package + * and that of Jonathan Shewchuk's adaptive precision floating point + * arithmetic package. See + * + * http://www.nersc.gov/~dhbailey/mpdist/mpdist.html + * http://www.cs.cmu.edu/~quake/robust.html + * + * for more details. + * + * Yozo Hida + */ +#ifndef _QD_QD_REAL_H +#define _QD_QD_REAL_H + +#include +#include +#include +#include "qd_config.h" +#include "dd_real.h" + +struct QD_API qd_real { + double x[4]; /* The Components. */ + + /* Eliminates any zeros in the middle component(s). */ + void zero_elim(); + void zero_elim(double &e); + + void renorm(); + void renorm(double &e); + + void quick_accum(double d, double &e); + void quick_prod_accum(double a, double b, double &e); + + qd_real(double x0, double x1, double x2, double x3); + explicit qd_real(const double *xx); + + static const qd_real _2pi; + static const qd_real _pi; + static const qd_real _3pi4; + static const qd_real _pi2; + static const qd_real _pi4; + static const qd_real _e; + static const qd_real _log2; + static const qd_real _log10; + static const qd_real _nan; + static const qd_real _inf; + + static const double _eps; + static const double _min_normalized; + static const qd_real _max; + static const qd_real _safe_max; + static const int _ndigits; + + qd_real(); + qd_real(const char *s); + qd_real(const dd_real &dd); + qd_real(double d); + qd_real(int i); + + double operator[](int i) const; + double &operator[](int i); + + static void error(const char *msg); + + bool isnan() const; + bool isfinite() const { return QD_ISFINITE(x[0]); } + bool isinf() const { return QD_ISINF(x[0]); } + + static qd_real ieee_add(const qd_real &a, const qd_real &b); + static qd_real sloppy_add(const qd_real &a, const qd_real &b); + + qd_real &operator+=(double a); + qd_real &operator+=(const dd_real &a); + qd_real &operator+=(const qd_real &a); + + qd_real &operator-=(double a); + qd_real &operator-=(const dd_real &a); + qd_real &operator-=(const qd_real &a); + + static qd_real sloppy_mul(const qd_real &a, const qd_real &b); + static qd_real accurate_mul(const qd_real &a, const qd_real &b); + + qd_real &operator*=(double a); + qd_real &operator*=(const dd_real &a); + qd_real &operator*=(const qd_real &a); + + static qd_real sloppy_div(const qd_real &a, const dd_real &b); + static qd_real accurate_div(const qd_real &a, const dd_real &b); + static qd_real sloppy_div(const qd_real &a, const qd_real &b); + static qd_real accurate_div(const qd_real &a, const qd_real &b); + + qd_real &operator/=(double a); + qd_real &operator/=(const dd_real &a); + qd_real &operator/=(const qd_real &a); + + qd_real operator^(int n) const; + + qd_real operator-() const; + + qd_real &operator=(double a); + qd_real &operator=(const dd_real &a); + qd_real &operator=(const char *s); + + bool is_zero() const; + bool is_one() const; + bool is_positive() const; + bool is_negative() const; + + explicit operator bool() const; // new + explicit operator double() const; // new + + static qd_real rand(void); + + void to_digits(char *s, int &expn, int precision = _ndigits) const; + void write(char *s, int len, int precision = _ndigits, + bool showpos = false, bool uppercase = false) const; + std::string to_string(int precision = _ndigits, int width = 0, + std::ios_base::fmtflags fmt = static_cast(0), + bool showpos = false, bool uppercase = false, char fill = ' ') const; + static int read(const char *s, qd_real &a); + + /* Debugging methods */ + void dump(const std::string &name, std::ostream &os = std::cerr) const; + void dump_bits(const std::string &name, + std::ostream &os = std::cerr) const; + + static qd_real debug_rand(); + +}; + +namespace std { + template <> + class numeric_limits : public numeric_limits { + public: + inline static double epsilon() { return qd_real::_eps; } + inline static double min() { return qd_real::_min_normalized; } + inline static qd_real max() { return qd_real::_max; } + inline static qd_real safe_max() { return qd_real::_safe_max; } + static const int digits = 209; + static const int digits10 = 62; + }; +} + +QD_API qd_real polyeval(const qd_real *c, int n, const qd_real &x); +QD_API qd_real polyroot(const qd_real *c, int n, + const qd_real &x0, int max_iter = 64, double thresh = 0.0); + +QD_API qd_real qdrand(void); +QD_API qd_real sqrt(const qd_real &a); + +QD_API inline bool isnan(const qd_real &a) { return a.isnan(); } +QD_API inline bool isfinite(const qd_real &a) { return a.isfinite(); } +QD_API inline bool isinf(const qd_real &a) { return a.isinf(); } + +/* Computes qd * d where d is known to be a power of 2. + This can be done component wise. */ +QD_API qd_real mul_pwr2(const qd_real &qd, double d); + +QD_API qd_real operator+(const qd_real &a, const qd_real &b); +QD_API qd_real operator+(const dd_real &a, const qd_real &b); +QD_API qd_real operator+(const qd_real &a, const dd_real &b); +QD_API qd_real operator+(const qd_real &a, double b); +QD_API qd_real operator+(double a, const qd_real &b); + +QD_API qd_real operator-(const qd_real &a, const qd_real &b); +QD_API qd_real operator-(const dd_real &a, const qd_real &b); +QD_API qd_real operator-(const qd_real &a, const dd_real &b); +QD_API qd_real operator-(const qd_real &a, double b); +QD_API qd_real operator-(double a, const qd_real &b); + +QD_API qd_real operator*(const qd_real &a, const qd_real &b); +QD_API qd_real operator*(const dd_real &a, const qd_real &b); +QD_API qd_real operator*(const qd_real &a, const dd_real &b); +QD_API qd_real operator*(const qd_real &a, double b); +QD_API qd_real operator*(double a, const qd_real &b); + +QD_API qd_real operator/(const qd_real &a, const qd_real &b); +QD_API qd_real operator/(const dd_real &a, const qd_real &b); +QD_API qd_real operator/(const qd_real &a, const dd_real &b); +QD_API qd_real operator/(const qd_real &a, double b); +QD_API qd_real operator/(double a, const qd_real &b); + +QD_API qd_real sqr(const qd_real &a); +QD_API qd_real sqrt(const qd_real &a); +QD_API qd_real pow(const qd_real &a, int n); +QD_API qd_real pow(const qd_real &a, const qd_real &b); +QD_API qd_real npwr(const qd_real &a, int n); + +QD_API qd_real nroot(const qd_real &a, int n); + +QD_API qd_real rem(const qd_real &a, const qd_real &b); +QD_API qd_real drem(const qd_real &a, const qd_real &b); +QD_API qd_real divrem(const qd_real &a, const qd_real &b, qd_real &r); + +dd_real to_dd_real(const qd_real &a); +double to_double(const qd_real &a); +int to_int(const qd_real &a); + +QD_API bool operator==(const qd_real &a, const qd_real &b); +QD_API bool operator==(const qd_real &a, const dd_real &b); +QD_API bool operator==(const dd_real &a, const qd_real &b); +QD_API bool operator==(double a, const qd_real &b); +QD_API bool operator==(const qd_real &a, double b); + +QD_API bool operator<(const qd_real &a, const qd_real &b); +QD_API bool operator<(const qd_real &a, const dd_real &b); +QD_API bool operator<(const dd_real &a, const qd_real &b); +QD_API bool operator<(double a, const qd_real &b); +QD_API bool operator<(const qd_real &a, double b); + +QD_API bool operator>(const qd_real &a, const qd_real &b); +QD_API bool operator>(const qd_real &a, const dd_real &b); +QD_API bool operator>(const dd_real &a, const qd_real &b); +QD_API bool operator>(double a, const qd_real &b); +QD_API bool operator>(const qd_real &a, double b); + +QD_API bool operator<=(const qd_real &a, const qd_real &b); +QD_API bool operator<=(const qd_real &a, const dd_real &b); +QD_API bool operator<=(const dd_real &a, const qd_real &b); +QD_API bool operator<=(double a, const qd_real &b); +QD_API bool operator<=(const qd_real &a, double b); + +QD_API bool operator>=(const qd_real &a, const qd_real &b); +QD_API bool operator>=(const qd_real &a, const dd_real &b); +QD_API bool operator>=(const dd_real &a, const qd_real &b); +QD_API bool operator>=(double a, const qd_real &b); +QD_API bool operator>=(const qd_real &a, double b); + +QD_API bool operator!=(const qd_real &a, const qd_real &b); +QD_API bool operator!=(const qd_real &a, const dd_real &b); +QD_API bool operator!=(const dd_real &a, const qd_real &b); +QD_API bool operator!=(double a, const qd_real &b); +QD_API bool operator!=(const qd_real &a, double b); + +QD_API qd_real fabs(const qd_real &a); +QD_API qd_real abs(const qd_real &a); /* same as fabs */ + +QD_API qd_real ldexp(const qd_real &a, int n); + +QD_API qd_real nint(const qd_real &a); +QD_API qd_real quick_nint(const qd_real &a); +QD_API qd_real floor(const qd_real &a); +QD_API qd_real ceil(const qd_real &a); +QD_API qd_real aint(const qd_real &a); + +QD_API qd_real sin(const qd_real &a); +QD_API qd_real cos(const qd_real &a); +QD_API qd_real tan(const qd_real &a); +QD_API void sincos(const qd_real &a, qd_real &s, qd_real &c); + +QD_API qd_real asin(const qd_real &a); +QD_API qd_real acos(const qd_real &a); +QD_API qd_real atan(const qd_real &a); +QD_API qd_real atan2(const qd_real &y, const qd_real &x); + +QD_API qd_real exp(const qd_real &a); +QD_API qd_real log(const qd_real &a); +QD_API qd_real log10(const qd_real &a); + +QD_API qd_real sinh(const qd_real &a); +QD_API qd_real cosh(const qd_real &a); +QD_API qd_real tanh(const qd_real &a); +QD_API void sincosh(const qd_real &a, qd_real &sin_qd, qd_real &cos_qd); + +QD_API qd_real asinh(const qd_real &a); +QD_API qd_real acosh(const qd_real &a); +QD_API qd_real atanh(const qd_real &a); + +QD_API qd_real qdrand(void); + +QD_API qd_real max(const qd_real &a, const qd_real &b); +QD_API qd_real max(const qd_real &a, const qd_real &b, const qd_real &c); +QD_API qd_real min(const qd_real &a, const qd_real &b); +QD_API qd_real min(const qd_real &a, const qd_real &b, const qd_real &c); + +QD_API qd_real fmod(const qd_real &a, const qd_real &b); + +QD_API std::ostream &operator<<(std::ostream &s, const qd_real &a); +QD_API std::istream &operator>>(std::istream &s, qd_real &a); +#ifdef QD_INLINE +#include "qd_inline.h" +#endif + +#endif /* _QD_QD_REAL_H */ + diff --git a/external/PackedCSparse/qd/util.cc b/external/PackedCSparse/qd/util.cc new file mode 100644 index 000000000..ab9620817 --- /dev/null +++ b/external/PackedCSparse/qd/util.cc @@ -0,0 +1,22 @@ +#include +#include "util.h" + +void append_expn(std::string &str, int expn) { + int k; + + str += (expn < 0 ? '-' : '+'); + expn = std::abs(expn); + + if (expn >= 100) { + k = (expn / 100); + str += '0' + k; + expn -= 100*k; + } + + k = (expn / 10); + str += '0' + k; + expn -= 10*k; + + str += '0' + expn; +} + diff --git a/external/PackedCSparse/qd/util.h b/external/PackedCSparse/qd/util.h new file mode 100644 index 000000000..7de358363 --- /dev/null +++ b/external/PackedCSparse/qd/util.h @@ -0,0 +1,4 @@ +#include + +void append_expn(std::string &str, int expn); + diff --git a/external/cmake-files/QD.cmake b/external/cmake-files/QD.cmake index b65e4f51c..a2e3e5e6d 100644 --- a/external/cmake-files/QD.cmake +++ b/external/cmake-files/QD.cmake @@ -1,54 +1,18 @@ set(QD_CMAKE_DIR ${CMAKE_CURRENT_LIST_DIR}) function(GetQD) - find_path(QD_DIR NAMES config.h PATHS ${QD_CMAKE_DIR}/../_deps/qd-src/) - - if (NOT QD_DIR) - include(FetchContent) - set(FETCHCONTENT_BASE_DIR "${QD_CMAKE_DIR}/../_deps") - FetchContent_Declare( - qd - URL https://www.davidhbailey.com/dhbsoftware/qd-2.3.23.tar.gz - ) - - FetchContent_GetProperties(qd) - - if(NOT qd_POPULATED) - message(STATUS "QD library not found locally, downloading it.") - FetchContent_Populate(qd) - endif() - - set(QD_DIR "${qd_SOURCE_DIR}") - message(STATUS "Using downloaded QD at: ${QD_DIR}") - - else() - - message(STATUS "QD library found: ${QD_DIR}") - - endif() - - include_directories(BEFORE "${QD_DIR}/include/") - message(STATUS "configuring the QD library") - execute_process( - COMMAND ./configure - WORKING_DIRECTORY ${QD_DIR} - OUTPUT_FILE CMD_OUTPUT - RESULT_VARIABLE EXECUTE - ) - if(NOT ${EXECUTE} EQUAL "0") - message(FATAL_ERROR "./configure QD library failed") - endif() - - execute_process( - COMMAND make - WORKING_DIRECTORY ${QD_DIR} - OUTPUT_FILE qd_compilation.txt - RESULT_VARIABLE EXECUTE_MAKE - ) - - if(NOT ${EXECUTE_MAKE} EQUAL "0") - message(FATAL_ERROR "building the QD library failed") - endif() - - find_library(QD_LIB NAMES libqd.a PATHS "${QD_DIR}/src/.libs") + set(QD_DIR "${QD_CMAKE_DIR}/../PackedCSparse/qd") + include_directories(BEFORE "${QD_DIR}") + set(QD_SOURCES + ${QD_DIR}/bits.cc + ${QD_DIR}/c_dd.cc + ${QD_DIR}/c_qd.cc + ${QD_DIR}/dd_const.cc + ${QD_DIR}/dd_real.cc + ${QD_DIR}/fpu.cc + ${QD_DIR}/qd_const.cc + ${QD_DIR}/qd_real.cc + ${QD_DIR}/util.cc + ) + add_library(QD_LIB ${QD_SOURCES}) endfunction() diff --git a/include/ode_solvers/implicit_midpoint.hpp b/include/ode_solvers/implicit_midpoint.hpp index 71408e542..786734d84 100644 --- a/include/ode_solvers/implicit_midpoint.hpp +++ b/include/ode_solvers/implicit_midpoint.hpp @@ -53,16 +53,15 @@ inline std::vector operator-(const std::vector &v1, return v1 + v2 * (-1.0); } -template +template struct ImplicitMidpointODESolver { using VT = typename Polytope::VT; using MT = typename Polytope::MT; - using pts = std::vector; - using hamiltonian = Hamiltonian; + using pts = std::vector; + using hamiltonian = Hamiltonian; using Opts = opts; unsigned int dim; - NT eta; int num_steps = 0; NT t; @@ -75,17 +74,14 @@ struct ImplicitMidpointODESolver { func F; Polytope &P; Opts &options; - VT nu; - + MT nu; + int num_runs = 0; hamiltonian ham; - bool done; #ifdef TIME_KEEPING std::chrono::time_point start, end; - std::chrono::duration DU_duration = - std::chrono::duration::zero(); - std::chrono::duration approxDK_duration = - std::chrono::duration::zero(); + std::chrono::duration DU_duration = std::chrono::duration::zero(); + std::chrono::duration approxDK_duration = std::chrono::duration::zero(); #endif ImplicitMidpointODESolver(NT initial_time, NT step, @@ -101,10 +97,11 @@ struct ImplicitMidpointODESolver { options(user_options), ham(hamiltonian(boundaries)) { - dim = xs[0].dimension(); + dim = xs[0].rows(); }; void step(int k, bool accepted) { + num_runs++; pts partialDerivatives; #ifdef TIME_KEEPING start = std::chrono::system_clock::now(); @@ -117,7 +114,7 @@ struct ImplicitMidpointODESolver { xs = xs + partialDerivatives * (eta / 2); xs_prev = xs; done = false; - nu = VT::Zero(P.equations()); + nu = MT::Zero(P.equations(), simdLen); for (int i = 0; i < options.maxODEStep; i++) { pts xs_old = xs; pts xmid = (xs_prev + xs) / 2.0; @@ -125,20 +122,21 @@ struct ImplicitMidpointODESolver { start = std::chrono::system_clock::now(); #endif partialDerivatives = ham.approxDK(xmid, nu); + #ifdef TIME_KEEPING end = std::chrono::system_clock::now(); approxDK_duration += end - start; #endif xs = xs_prev + partialDerivatives * (eta); - NT dist = ham.x_norm(xmid, xs - xs_old) / eta; - NT maxdist = dist; + VT dist = ham.x_norm(xmid, xs - xs_old) / eta; + NT maxdist = dist.maxCoeff(); //If the estimate does not change terminate if (maxdist < options.implicitTol) { done = true; num_steps = i; break; - //If the estimate is very bad sample another velocity - } else if (maxdist > options.convergence_limit) { + //If the estimate is very bad, sample another velocity + } else if (maxdist > options.convergence_bound) { xs = xs * std::nan("1"); done = true; num_steps = i; @@ -163,16 +161,15 @@ struct ImplicitMidpointODESolver { } } - Point get_state(int index) { return xs[index]; } + MT get_state(int index) { return xs[index]; } - void set_state(int index, Point p) { xs[index] = p; } - void print_state() { + void set_state(int index, MT p) { xs[index] = p; } + template + void print_state(StreamType &stream) { for (int j = 0; j < xs.size(); j++) { - std::cerr << "state " << j << ": "; - for (unsigned int i = 0; i < xs[j].dimension(); i++) { - std::cerr << xs[j][i] << " "; - } - std::cerr << '\n'; + stream << "state " << j << ": \n"; + stream << xs[j]; + stream << '\n'; } } }; diff --git a/include/ode_solvers/oracle_functors.hpp b/include/ode_solvers/oracle_functors.hpp index 056e96c3a..3aea450b2 100644 --- a/include/ode_solvers/oracle_functors.hpp +++ b/include/ode_solvers/oracle_functors.hpp @@ -142,11 +142,11 @@ struct IsotropicQuadraticFunctor { return xs[i + 1]; // returns derivative } } - - Point operator()(Point const&x){ - Point y = (-params.alpha)*x; - return y; - } + + Point operator()(Point const &x){ + Point y = (-params.alpha) * x; + return y; + } }; diff --git a/include/ode_solvers/oracle_functors_rcpp.hpp b/include/ode_solvers/oracle_functors_rcpp.hpp index 558ac1b07..853dcb3c0 100644 --- a/include/ode_solvers/oracle_functors_rcpp.hpp +++ b/include/ode_solvers/oracle_functors_rcpp.hpp @@ -91,6 +91,17 @@ struct RcppFunctor { } } + Point operator() (Point const& x) const { + VT y = Rcpp::as(neg_grad_f(Rcpp::wrap(x.getCoefficients()))); + + Point z(y); + + if (negate) z = (-1.0) * z; + + // Return result as Point + return z; + } + }; // Negative log-probability functor @@ -118,6 +129,31 @@ struct RcppFunctor { }; + // Log-probability hessian functor + template + < + typename Point + > + struct HessianFunctor { + typedef typename Point::FT NT; + typedef typename Point::Coeff VT; + + parameters params; + Rcpp::Function hessian; // Negative hessian as an Rcpp::Function + + HessianFunctor( + parameters params_, + Rcpp::Function hessian_) : + params(params_), + hessian(hessian_) + {}; + + Point operator() (Point const& x) const { + VT y= Rcpp::as(hessian(Rcpp::wrap(x.getCoefficients()))); + return Point(y); + } + + }; }; #endif diff --git a/include/preprocess/crhmc/analytic_center.h b/include/preprocess/crhmc/analytic_center.h index 35d4349aa..7124626b4 100644 --- a/include/preprocess/crhmc/analytic_center.h +++ b/include/preprocess/crhmc/analytic_center.h @@ -27,14 +27,6 @@ #endif const size_t chol_k2 = (SIMD_LEN == 0) ? 1 : SIMD_LEN; -using NT = double; -using MT = Eigen::Matrix; -using VT = Eigen::Matrix; -using SpMat = Eigen::SparseMatrix; -using CholObj = PackedChol; -using Triple = Eigen::Triplet; -using Tx = FloatArray; -using Opts = opts; /*This function computes the analytic center of the polytope*/ //And detects additional constraint that need to be added // x - It outputs the minimizer of min f(x) subjects to {Ax=b} @@ -43,9 +35,12 @@ using Opts = opts; // because of the dom(f), the algorithm will detect the collapsed dimension // and output the detected constraint C x = d // d - detected constraint vector -template +template std::tuple analytic_center(SpMat const &A, VT const &b, Polytope &f, Opts const &options, VT x = VT::Zero(0, 1)) { + using CholObj = typename Polytope::CholObj; + using Triple = typename Polytope::Triple; + using Tx = typename Polytope::Tx; // initial conditions int n = A.cols(); int m = A.rows(); @@ -65,7 +60,7 @@ std::tuple analytic_center(SpMat const &A, VT const &b, Polytope std::vector idx; CholObj solver = CholObj(transform_format(A)); - + solver.accuracyThreshold = 0; for (int iter = 0; iter < options.ipmMaxIter; iter++) { std::pair pair_analytic_oracle = f.analytic_center_oracle(x); @@ -159,14 +154,14 @@ std::tuple analytic_center(SpMat const &A, VT const &b, Polytope std::pair pboundary = f.barrier.boundary(x); VT A_ = pboundary.first; VT b_ = pboundary.second; - A_ = A_(idx); std::vector sparseIdx; for (int i = 0; i < idx.size(); i++) { - sparseIdx.push_back(Triple(i, i, A_(i))); + sparseIdx.push_back(Triple(i, idx[i], A_(idx[i]))); } C.setFromTriplets(sparseIdx.begin(), sparseIdx.end()); - d = b_(idx); + d.resize(idx.size(), 1); + copy_indicies(d, b_, idx); } else { diff --git a/include/preprocess/crhmc/constraint_problem.h b/include/preprocess/crhmc/constraint_problem.h new file mode 100644 index 000000000..829315059 --- /dev/null +++ b/include/preprocess/crhmc/constraint_problem.h @@ -0,0 +1,81 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2012-2020 Vissarion Fisikopoulos +// Copyright (c) 2018-2020 Apostolos Chalkis +// Copyright (c) 2022-2022 Ioannis Iakovidis + +// Contributed and/or modified by Ioannis Iakovidis, as part of Google Summer of +// Code 2022 program. + +// Licensed under GNU LGPL.3, see LICENCE file + +#ifndef CONSTRAINT_PROBLEM_H +#define CONSTRAINT_PROBLEM_H +#include "Eigen/Eigen" +/*Input structure: With this the user can define a polytope sampling problem*/ +template +class constraint_problem { +public: + using Type = typename Point::FT; + using PointType = Point; + using VT = Eigen::Matrix; + using MT = MatrixType; +private: + unsigned int num_vars; // num_vars of the original problem + MatrixType Aeq; // Matrix of coefficients for the equality constraints + VT beq; // Right hand side of the equality constraints + MatrixType Aineq; // Matrix of coefficients for the inequality constraints + VT bineq; // Right hand side of the inequality constraints + VT lb; // lb on the output coordinates preset to -1e9 + VT ub; // ub on the output coordinates preset to +1e9 + Type inf = 1e9; +public: + /*Constructors for different input instances*/ + constraint_problem(const int dim, MT const &Aeq_, VT const &beq_, MT const &Aineq_, + VT const &bineq_, VT const &lb_, VT const &ub_) + : num_vars(dim), Aeq(Aeq_), beq(beq_), Aineq(Aineq_), bineq(bineq_), + lb(lb_), ub(ub_) { + } + + constraint_problem(const int dim) { + num_vars = dim; + init(num_vars); + } + + + void init(int num_vars) { + Aineq.resize(0, num_vars); + Aeq.resize(0, num_vars); + bineq.resize(0, 1); + beq.resize(0, 1); + lb = -VT::Ones(num_vars) * inf; + ub = VT::Ones(num_vars) * inf; + } + void set_equality_constraints(MT const &Aeq_, VT const &beq_){ + Aeq = Aeq_; + beq = beq_; + } + void set_inequality_constraints(MT const &Aineq_, VT const &bineq_){ + Aineq = Aineq_; + bineq = bineq_; + } + void set_bounds(VT const &lb_, VT const &ub_){ + lb = lb_; + ub = ub_; + } + std::pair get_equations(){ + return std::make_pair(Aeq,beq); + } + std::pair get_inequalities(){ + return std::make_pair(Aineq,bineq); + } + std::pair get_bounds(){ + return std::make_pair(lb,ub); + } + unsigned int dimension(){ + return num_vars; + } + +}; + +#endif diff --git a/include/preprocess/crhmc/crhmc_input.h b/include/preprocess/crhmc/crhmc_input.h index 1a674f1e4..05f61c4b8 100644 --- a/include/preprocess/crhmc/crhmc_input.h +++ b/include/preprocess/crhmc/crhmc_input.h @@ -17,12 +17,20 @@ #define CRHMC_INPUT_H #include "Eigen/Eigen" #include "opts.h" +#include "convex_bodies/hpolytope.h" +#include "preprocess/crhmc/constraint_problem.h" /*0 funciton handles are given as a reference in case the user gives no function. Then the uniform function is implied*/ template struct ZeroFunctor { + using Type = typename Point::FT; Point operator()(Point const &x) const { return Point(x.dimension()); } + struct parameters { + Type L=1; + Type eta=1; + }; + struct parameters params; }; template struct ZeroScalarFunctor @@ -45,9 +53,11 @@ class crhmc_input ZeroScalarFunctor zerosf; public: + using MT = MatrixType; using Func = func; using Grad = grad; using Hess = hess; + using point= Point; MatrixType Aineq; // Matrix of coefficients for the inequality constraints VT bineq; // Right hand side of the inequality constraints MatrixType Aeq; // Matrix of coefficients for the equality constraints @@ -62,37 +72,38 @@ class crhmc_input bool fHandle; // whether f is handle or not bool dfHandle; // whether df is handle or not bool ddfHandle; // whether ddf is handle or not + unsigned int dimension; // dimension of the original problem const Type inf = options.max_coord + 1; // helper for barrier handling /*Constructors for different input instances*/ - crhmc_input(int dimension, func &function, grad &g, hess &h) + crhmc_input(int dim, func &function, grad &g, hess &h) : f(function), df(g), ddf(h) - { + { dimension=dim; fZero = false; fHandle = true; dfHandle = true; ddfHandle = true; init(dimension); } - crhmc_input(int dimension, func &function) + crhmc_input(int dim, func &function) : f(function), df(zerof), ddf(zerof) - { + { dimension=dim; fZero = false; fHandle = true; dfHandle = false; ddfHandle = false; init(dimension); } - crhmc_input(int dimension, func &function, grad &g) + crhmc_input(int dim, func &function, grad &g) : f(function), df(g), ddf(zerof) - { + { dimension=dim; fZero = false; fHandle = true; dfHandle = true; ddfHandle = false; init(dimension); } - crhmc_input(int dimension) : f(zerosf), df(zerof), ddf(zerof) - { + crhmc_input(int dim) : f(zerosf), df(zerof), ddf(zerof) + { dimension=dim; fZero = true; fHandle = false; dfHandle = false; @@ -110,4 +121,52 @@ class crhmc_input ub = VT::Ones(dimension) * inf; } }; +#include + +template < + typename Input, typename Polytope, typename Func, typename Grad, typename Hess, + typename std::enable_if>::value>::type * = nullptr> +inline Input convert2crhmc_input(Polytope &P, Func &f, Grad &g, Hess &h) { + int dimension = P.dimension(); + Input input = Input(dimension, f, g, h); + if (std::is_same< + Hess, ZeroFunctor>::value){ + input.ddfHandle=false; + } + input.Aineq = P.get_mat(); + input.bineq = P.get_vec(); + return input; +} + +template >::value>::type + * = nullptr> +inline Input convert2crhmc_input(Polytope &P, Func &f, Grad &g, Hess &h) { + int dimension = P.dimension(); + Input input = Input(dimension, f, g, h); + if (std::is_same< + Hess, ZeroFunctor>::value){ + input.ddfHandle=false; + } + std::tie(input.Aineq, input.bineq) = P.get_inequalities(); + std::tie(input.Aeq, input.beq) = P.get_equations(); + std::tie(input.lb, input.ub) = P.get_bounds(); + return input; +} +template >::value && + !std::is_same>::value>:: + type * = nullptr> +inline Input convert2crhmc_input(Polytope &P, Func &f, Grad &g, Hess &h) { + /*CRHMC works only for H-polytopes and constraint_problems for now*/ + int dimension = 0; + Input input = Input(dimension, f, g, h); + return input; +} #endif diff --git a/include/preprocess/crhmc/crhmc_problem.h b/include/preprocess/crhmc/crhmc_problem.h index 68ccb25e5..c501bce62 100644 --- a/include/preprocess/crhmc/crhmc_problem.h +++ b/include/preprocess/crhmc/crhmc_problem.h @@ -60,6 +60,7 @@ class crhmc_problem { using Func = typename Input::Func; using Grad = typename Input::Grad; using Hess = typename Input::Hess; + using Crhmc_problem=crhmc_problem; unsigned int _d; // dimension // Problem variables Ax=b st lb<=x<=ub @@ -93,15 +94,20 @@ class crhmc_problem { bool fHandle; // whether f is handle or not bool dfHandle; // whether df is handle or not bool ddfHandle; // whether ddf is handle or not + /*Invalid polytope variables*/ + bool terminate=false; + std::string terminate_message; #ifdef TIME_KEEPING //Timing information std::chrono::duration rescale_duration, sparsify_duration, reordering_duration, rm_rows_duration, rm_fixed_vars_duration, ex_collapsed_vars_duration, shift_barrier_duration, lewis_center_duration; #endif - const NT inf = options.max_coord + 1; // helper for barrier handling + const NT inf = options.max_coord; // helper for barrier handling + const NT barrier_bound = 1e7; int equations() const { return Asp.rows(); } int dimension() const { return Asp.cols(); } + int nnz() const { return Asp.nonZeros(); } // Remove varibles that have width under some tolerance int remove_fixed_variables(const NT tol = 1e-12) { @@ -109,6 +115,7 @@ class crhmc_problem { int n = Asp.cols(); VT d = estimate_width(); CholObj solver = CholObj(transform_format(Asp)); + solver.accuracyThreshold = 0; VT w = VT::Ones(n, 1); solver.decompose((Tx *)w.data()); VT out_vector = VT(m, 1); @@ -133,7 +140,16 @@ class crhmc_problem { SpMat S = SpMat(n, freeIndices.size()); S.setFromTriplets(freeIndices.begin(), freeIndices.end()); append_map(S, x); - barrier.set_bound(barrier.lb(indices), barrier.ub(indices)); + copy_indicies(barrier.lb, barrier.lb, indices); + copy_indicies(barrier.ub, barrier.ub, indices); + barrier.lb.conservativeResize(indices.size()); + barrier.ub.conservativeResize(indices.size()); + + barrier.set_bound(barrier.lb, barrier.ub); + if (!isempty_center) { + copy_indicies(center, center, indices); + center.conservativeResize(indices.size()); + } return 1; } return 0; @@ -143,20 +159,20 @@ class crhmc_problem { SpMat Ac; VT bc; if (isempty_center) { - std::tie(center, Ac, bc) = analytic_center(Asp, b, *this, options); + std::tie(center, Ac, bc) = analytic_center(Asp, b, *this, options); isempty_center = false; } else { std::tie(center, Ac, bc) = - analytic_center(Asp, b, *this, options, center); + analytic_center(Asp, b, *this, options, center); analytic_ctr=center; } if (Ac.rows() == 0) { return 0; } - SpMat _A = Asp; - sparse_stack_v(Ac, _A, Asp); - b.resize(b.rows() + bc.rows(), 1); - b << bc, b; + SpMat _A = SpMat(Asp); + sparse_stack_v(_A, Ac, Asp); + b.conservativeResize(b.rows() + bc.rows(), 1); + b.bottomRows(bc.rows()) = bc; return 1; } // Rescale the polytope for numerical stability @@ -273,45 +289,41 @@ class crhmc_problem { // Reorder the polytope accordint to the AMD Reordering for better sparsity // pattern in the Cholesky decomposition void reorder() { - if (!options.EnableReordering) { + if (!options.EnableReordering || Asp.rows()*Asp.cols() < options.maxNZ) { return; } - Asp.prune(0.0); - Asp.makeCompressed(); - int m = Asp.rows(); - SpMat H; - H = Asp * SpMat(Asp.transpose()) + MT::Identity(m, m); - H.makeCompressed(); - PM permed = permuteMatAMD(H); - H = permed * H * permed.transpose(); - PM post_perm = postOrderPerm(H); - PM perm = permed * post_perm; - Asp = perm * Asp; - b = perm * b; + fillin_reduce(Asp,b); } //Using the Cholesky decomposition remove dependent rows in the systen Asp*x=b int remove_dependent_rows(NT tolerance = 1e-12, NT infinity = 1e+64) { - //this approach does not work with 0 collumns - remove_zero_rows(Asp); + //this approach does not work with 0 rows + remove_zero_rows(Asp, b); int m = Asp.rows(); int n = Asp.cols(); VT v = VT(m); VT w = VT::Ones(n, 1); CholObj solver = CholObj(transform_format(Asp)); + solver.accuracyThreshold = 0; solver.decompose((Tx *)w.data()); solver.diagL((Tx *)v.data()); - std::vector indices; + std::vector indices(m, false); + std::vector idx; + bool changed = false; for (int i = 0; i < m; i++) { if ((v(i) > tolerance) && (v(i) < infinity)) { - indices.push_back(i); + indices[i] = true; + idx.push_back(i); + }else{ + changed=true; } } - if (indices.size() == m) { + if (!changed) { return 0; } - Asp = A(indices, Eigen::all).sparseView(); - b = b(indices); + remove_rows(Asp, indices); + copy_indicies(b, b, idx); + b.conservativeResize(idx.size(), 1); return 1; } //Apply number of operations that simplify the problem @@ -373,10 +385,14 @@ class crhmc_problem { } } - VT estimate_width() { + VT estimate_width(bool use_center=false) { int n = Asp.cols(); VT hess = VT::Ones(n, 1); + if(use_center){ + std::tie(std::ignore, hess)=analytic_center_oracle(center); + } CholObj solver = CholObj(transform_format(Asp)); + solver.accuracyThreshold = 0; solver.decompose((Tx *)hess.data()); VT w_vector(n, 1); solver.leverageScoreComplement((Tx *)w_vector.data()); @@ -386,75 +402,110 @@ class crhmc_problem { return tau; } - void print(std::string const message = "Printing Sparse problem") { - std::cerr << "----------------" << message << "--------------" << '\n'; - std::cerr << "(m,n) = " << equations() << " , " << dimension() + template + void print(StreamType &stream, std::string const message = "Printing Sparse problem") { + stream << "----------------" << message << "--------------" << '\n'; + stream << "(m,n) = " << equations() << " , " << dimension() << " nnz= " << Asp.nonZeros() << "\n"; if (equations() > 20 || dimension() > 20) { - std::cerr << "too big for complete visulization\n"; + stream << "too big for complete visulization\n"; return; } - std::cerr << "A=\n"; + stream << "A=\n"; - std::cerr << MT(Asp); - std::cerr << "\n"; + stream << MT(Asp); + stream << "\n"; - std::cerr << "b=\n"; - std::cerr << b; - std::cerr << "\n"; + stream << "b=\n"; + stream << b; + stream << "\n"; - std::cerr << "lb=\n"; - std::cerr << barrier.lb; - std::cerr << "\n"; + stream << "lb=\n"; + stream << barrier.lb; + stream << "\n"; - std::cerr << "ub=\n"; - std::cerr << barrier.ub; - std::cerr << "\n"; + stream << "ub=\n"; + stream << barrier.ub; + stream << "\n"; - std::cerr << "T=\n"; - std::cerr << MT(T); - std::cerr << "\n"; + stream << "T=\n"; + stream << MT(T); + stream << "\n"; - std::cerr << "y=\n"; - std::cerr << y; - std::cerr << "\n"; + stream << "y=\n"; + stream << y; + stream << "\n"; - std::cerr << "center=\n"; - std::cerr << center; - std::cerr << "\n"; + stream << "center=\n"; + stream << center; + stream << "\n"; } - void print(const char *fileName) { - std::ofstream myfile; - myfile.open(fileName); - myfile << Asp.rows() << " " << Asp.cols() << "\n"; - - myfile << MT(Asp); - myfile << "\n"; - myfile << "\n"; - - myfile << b; - myfile << "\n"; - myfile << "\n"; - - myfile << barrier.lb; - myfile << "\n"; - myfile << "\n"; - - myfile << barrier.ub; - myfile << "\n"; - myfile << "\n"; - - myfile << MT(T); - myfile << "\n"; - myfile << "\n"; - - myfile << y; - myfile << "\n"; - myfile << "\n"; - - myfile << center; - } + void make_format(Input const &input, MT const &S) { + nP = input.Aeq.cols(); + int nIneq = input.Aineq.rows(); + int nEq = input.Aeq.rows(); + A.resize(nEq + nIneq, nP + nIneq); + A << input.Aeq, MT::Zero(nEq, nIneq), input.Aineq, MT::Identity(nIneq, nIneq); + b.resize(nEq + nIneq, 1); + b << input.beq, input.bineq; + lb.resize(nP + nIneq, 1); + ub.resize(nP + nIneq, 1); + lb << input.lb, MT::Zero(nIneq, 1); + ub << input.ub, MT::Ones(nIneq, 1) * inf; + Asp.resize(nEq + nIneq, nP + nIneq); + int n = dimension(); + /*Move lb=ub to Ax=b*/ + for (int i = 0; i < n; i++) { + if (doubleVectorEqualityComparison(lb(i), ub(i))) { + MT temp = MT::Zero(1, n); + temp(i) = 1; + A.conservativeResize(A.rows() + 1, A.cols()); + A.row(A.rows() - 1) = temp; + b.conservativeResize(b.rows() + 1); + b(b.rows() - 1) = (lb(i) + ub(i)) / 2; + lb(i) = -inf; + ub(i) = inf; + } + } + Asp = A.sparseView(); + } + void make_format(Input const &input, SpMat const &S) { + nP = input.Aeq.cols(); + int nIneq = input.Aineq.rows(); + int nEq = input.Aeq.rows(); + Asp.resize(nEq + nIneq, nP + nIneq); + SpMat B = SpMat(input.Aeq); + SpMat C = SpMat(input.Aineq); + B.conservativeResize(nEq, nIneq + nP); + SpMat temp = SpMat(nIneq, nIneq); + temp.setIdentity(); + sparse_stack_h_inplace(C, temp); + sparse_stack_v(B, C, Asp); + b.resize(nEq + nIneq, 1); + b << input.beq, input.bineq; + lb.resize(nP + nIneq, 1); + ub.resize(nP + nIneq, 1); + lb << input.lb, MT::Zero(nIneq, 1); + ub << input.ub, MT::Ones(nIneq, 1) * inf; + int n = dimension(); + /*Move lb=ub to Ax=b*/ + for (int i = 0; i < n; i++) { + if (doubleVectorEqualityComparison(lb(i), ub(i))) { + B.resize(Asp.rows(), Asp.cols()); + B = SpMat(Asp); + MT temp = MT::Zero(1, n); + temp(i) = 1; + C = temp.sparseView(); + sparse_stack_v(B, C, Asp); + b.conservativeResize(b.rows() + 1); + b(b.rows() - 1) = (lb(i) + ub(i)) / 2; + lb(i) = -inf; + ub(i) = inf; + } + } + Asp.makeCompressed(); + } //Class constructor crhmc_problem(Input const &input, Opts _options = Opts()) : options(_options), func(input.f), df(input.df), ddf(input.ddf), @@ -467,41 +518,13 @@ class crhmc_problem { std::chrono::duration::zero(); #endif - nP = input.Aeq.cols(); - int nIneq = input.Aineq.rows(); - int nEq = input.Aeq.rows(); - A.resize(nEq + nIneq, nP + nIneq); - A << input.Aeq, MT::Zero(nEq, nIneq), input.Aineq, - MT::Identity(nIneq, nIneq); - b.resize(nEq + nIneq, 1); - b << input.beq, input.bineq; - lb.resize(nP + nIneq, 1); - ub.resize(nP + nIneq, 1); - lb << input.lb, MT::Zero(nIneq, 1); - ub << input.ub, MT::Ones(nIneq, 1) * inf; - Asp.resize(nEq + nIneq, nP + nIneq); + make_format(input, input.Aeq); PreproccessProblem(); } // Initialization funciton void PreproccessProblem() { int n = dimension(); - /*Move lb=ub to Ax=b*/ - for (int i = 0; i < n; i++) { - if (doubleVectorEqualityComparison(lb(i), ub(i))) { - MT temp = MT::Zero(1, n); - temp(i) = 1; - A.conservativeResize(A.rows() + 1, A.cols()); - A.row(A.rows() - 1) = temp; - b.conservativeResize(b.rows() + 1); - b(b.rows() - 1) = (lb(i) + ub(i)) / 2; - lb(i) = -inf; - ub(i) = inf; - } - } - - barrier.set_bound(lb.cwiseMax(-1e7), ub.cwiseMin(1e7)); - - Asp = A.sparseView(); + barrier.set_bound(lb.cwiseMax(-barrier_bound), ub.cwiseMin(barrier_bound)); NT tol = std::numeric_limits::epsilon(); Asp.prune(tol, tol); /*Update the transformation Tx + y*/ @@ -527,7 +550,7 @@ class crhmc_problem { #endif if (isempty_center) { std::tie(center, std::ignore, std::ignore) = - analytic_center(Asp, b, *this, options); + analytic_center(Asp, b, *this, options); isempty_center = false; } shift_barrier(center); @@ -537,22 +560,17 @@ class crhmc_problem { #endif reorder(); - width = estimate_width(); - if (width.maxCoeff() > 1e9) { - std::cerr << "Domain seems to be unbounded. Either add a Gaussian term " - "via f, df, ddf or add bounds to variable via lb and ub." - << '\n'; - exit(1); - } + width = estimate_width(true); // Recenter again and make sure it is feasible VT hess; #ifdef TIME_KEEPING start = std::chrono::system_clock::now(); #endif std::tie(center, std::ignore, std::ignore, w_center) = - lewis_center(Asp, b, *this, options, center); + lewis_center(Asp, b, *this, options, center); std::tie(std::ignore, hess) = lewis_center_oracle(center, w_center); CholObj solver = CholObj(transform_format(Asp)); + solver.accuracyThreshold = 0; VT Hinv = hess.cwiseInverse(); solver.decompose((Tx *)Hinv.data()); VT out(equations(), 1); @@ -565,81 +583,101 @@ class crhmc_problem { #endif if ((center.array() > barrier.ub.array()).any() || (center.array() < barrier.lb.array()).any()) { - std::cerr << "Polytope:Infeasible. The algorithm cannot find a feasible " - "point.\n"; - exit(1); + terminate = true; + terminate_message = "Polytope:Infeasible. The algorithm cannot find a feasible point.\n"; + return; } + } #ifdef TIME_KEEPING - std::cerr << "Rescale completed in time, "; - std::cerr << rescale_duration.count() << " secs " << std::endl; - std::cerr << "Split dense columns completed in time, "; - std::cerr << sparsify_duration.count() << " secs " << std::endl; - std::cerr << "Reordering completed in time, "; - std::cerr << reordering_duration.count() << " secs " << std::endl; - std::cerr << "Removing dependent rows completed in time, "; - std::cerr << rm_rows_duration.count() << " secs " << std::endl; - std::cerr << "Removing fixed variables completed in time, "; - std::cerr << rm_fixed_vars_duration.count() << " secs " << std::endl; - std::cerr << "Extracting collapsed variables completed in time, "; - std::cerr << ex_collapsed_vars_duration.count() << " secs " << std::endl; - std::cerr << "Shift_barrier completed in time, "; - std::cerr << shift_barrier_duration.count() << " secs " << std::endl; - std::cerr << "Finding Center completed in time, "; - std::cerr << lewis_center_duration.count() << " secs " << std::endl; +template +void print_preparation_time(StreamType& stream){ + stream << "---Preparation Timing Information"<< std::endl; + stream << "Rescale completed in time, "; + stream << rescale_duration.count() << " secs " << std::endl; + stream << "Split dense columns completed in time, "; + stream << sparsify_duration.count() << " secs " << std::endl; + stream << "Reordering completed in time, "; + stream << reordering_duration.count() << " secs " << std::endl; + stream << "Removing dependent rows completed in time, "; + stream << rm_rows_duration.count() << " secs " << std::endl; + stream << "Removing fixed variables completed in time, "; + stream << rm_fixed_vars_duration.count() << " secs " << std::endl; + stream << "Extracting collapsed variables completed in time, "; + stream << ex_collapsed_vars_duration.count() << " secs " << std::endl; + stream << "Shift_barrier completed in time, "; + stream << shift_barrier_duration.count() << " secs " << std::endl; + stream << "Finding Center completed in time, "; + stream << lewis_center_duration.count() << " secs " << std::endl; +} #endif - } - // Gradient and hessian of for the analytic center std::pair analytic_center_oracle(VT const &x) { - VT g, h; + MT g, h; std::tie(std::ignore, g, h) = f_oracle(x); return std::make_pair(g + barrier.gradient(x), h + barrier.hessian(x)); } // Gradient and hessian of for the lewis center std::pair lewis_center_oracle(VT const &x, VT const &w) { - VT g, h; + MT g, h; std::tie(std::ignore, g, h) = f_oracle(x); return std::make_pair(g + w.cwiseProduct(barrier.gradient(x)), h + w.cwiseProduct(barrier.hessian(x))); } // Function that uses the transformation (T,y) to apply the function to the // original variables - std::tuple f_oracle(VT x) { - NT f; - VT g, h; + std::tuple f_oracle(MT const &x) { int n = x.rows(); + int m=x.cols(); + VT f=VT(m); + MT g=MT(n,m); + MT h=MT(n,m); if (fZero) { - f = 0; - g = VT::Zero(n); - h = VT::Zero(n); + f = VT::Zero(m); + g = MT::Zero(n,m); + h = MT::Zero(n,m); return std::make_tuple(f, g, h); } // Take the correpsonding point in the original space - VT z = VT::Zero(n); + MT z = MT::Zero(y.rows(), m); if (fHandle || dfHandle || ddfHandle) { - z(Tidx, Eigen::all) = Ta.cwiseProduct(x(Tidx, Eigen::all)) + y; + for(int k=0;k #include "PackedCSparse/SparseMatrix.h" #include #include @@ -93,6 +94,7 @@ void sparse_stack_h(const SparseMatrixType &left, const SparseMatrixType &right, [&](int i) { return i + left.nonZeros(); }); } +#include template void sparse_stack_h_inplace(SparseMatrixType &left, @@ -116,16 +118,13 @@ void sparse_stack_h_inplace(SparseMatrixType &left, { return i + leftnz; }); } -template -void remove_zero_rows(SparseMatrixType &A) -{ +template +void remove_zero_rows(SparseMatrixType &A, VectorType &b) { std::vector> tripletList; unsigned Ndata = A.cols(); unsigned Nbins = A.rows(); - for (int k = 0; k < A.outerSize(); ++k) - { - for (typename SparseMatrixType::InnerIterator it(A, k); it; ++it) - { + for (int k = 0; k < A.outerSize(); ++k) { + for (typename SparseMatrixType::InnerIterator it(A, k); it; ++it) { tripletList.push_back( Eigen::Triplet(it.row(), it.col(), it.value())); } @@ -134,20 +133,19 @@ void remove_zero_rows(SparseMatrixType &A) std::vector has_value(Nbins, false); for (auto tr : tripletList) has_value[tr.row()] = true; - if (std::all_of(has_value.begin(), has_value.end(), - [](bool v) - { return v; })) - { + [](bool v) { return v; })) { return; } // create map from old to new indices std::map row_map; unsigned new_idx = 0; for (unsigned old_idx = 0; old_idx < Nbins; old_idx++) - if (has_value[old_idx]) - row_map[old_idx] = new_idx++; - + if (has_value[old_idx]) { + row_map[old_idx] = new_idx; + b(new_idx) = b(old_idx); + new_idx++; + } // make new triplet list, dropping empty rows std::vector> newTripletList; newTripletList.reserve(Ndata); @@ -156,36 +154,15 @@ void remove_zero_rows(SparseMatrixType &A) Eigen::Triplet(row_map[tr.row()], tr.col(), tr.value())); // form new matrix and return - SparseMatrixType ret(new_idx, Ndata); - ret.setFromTriplets(newTripletList.begin(), newTripletList.end()); - A = SparseMatrixType(ret); + A.resize(new_idx, Ndata); + A.setFromTriplets(newTripletList.begin(), newTripletList.end()); + b.conservativeResize(new_idx); } template -void remove_rows(SparseMatrixType &A, std::vector indices) -{ - std::vector> tripletList; +void remove_rows(SparseMatrixType &A, std::vector ¬Removed) { unsigned Ndata = A.cols(); unsigned Nbins = A.rows(); - for (int k = 0; k < A.outerSize(); ++k) - { - for (typename SparseMatrixType::InnerIterator it(A, k); it; ++it) - { - tripletList.push_back( - Eigen::Triplet(it.row(), it.col(), it.value())); - } - } - - std::vector notRemoved(Nbins, false); - for (auto tr : indices) - notRemoved[tr] = true; - - if (std::all_of(notRemoved.begin(), notRemoved.end(), - [](bool v) - { return v; })) - { - return; - } // create map from old to new indices std::map row_map; unsigned new_idx = 0; @@ -193,17 +170,18 @@ void remove_rows(SparseMatrixType &A, std::vector indices) if (notRemoved[old_idx]) row_map[old_idx] = new_idx++; - // make new triplet list, dropping empty rows - std::vector> newTripletList; - newTripletList.reserve(Ndata); - for (auto tr : tripletList) - newTripletList.push_back( - Eigen::Triplet(row_map[tr.row()], tr.col(), tr.value())); - + std::vector> tripletList; + for (int k = 0; k < A.outerSize(); ++k) { + for (typename SparseMatrixType::InnerIterator it(A, k); it; ++it) { + if (notRemoved[it.row()]) { + tripletList.push_back( + Eigen::Triplet(row_map[it.row()], it.col(), it.value())); + } + } + } // form new matrix and return - SparseMatrixType ret(new_idx, Ndata); - ret.setFromTriplets(newTripletList.begin(), newTripletList.end()); - A = SparseMatrixType(ret); + A.resize(new_idx, Ndata); + A.setFromTriplets(tripletList.begin(), tripletList.end()); } template @@ -352,6 +330,17 @@ PM postOrderPerm(SparseMatrixType const &A) return post_perm; } +template +void fillin_reduce(SparseMatrixType &X,VectorType& b){ + SparseMatrixType I = SparseMatrixType(Eigen::VectorXd::Ones(X.rows()).asDiagonal()); + SparseMatrixType XX = X * X.transpose() + I; + XX.makeCompressed(); + Eigen::SimplicialLDLT> cholesky; + cholesky.analyzePattern(XX); + X = cholesky.permutationP() * X; + b = cholesky.permutationP() *b; +} template PackedCSparse::SparseMatrix transform_format(SparseMatrixType const &mat) { PackedCSparse::SparseMatrix A = PackedCSparse::SparseMatrix(mat.rows(), mat.cols(), mat.nonZeros()); @@ -367,5 +356,51 @@ PackedCSparse::SparseMatrix transform_format(SparseMatrixType co A.p[A.n] = nnz; return A; } - +template +void copy_indicies(MatrixType& a, MatrixType& b, std::vectorconst & a_idx, std::vectorconst & b_idx){ +for(int i=0;i +void copy_indicies(MatrixType& a, MatrixType b, std::vectorconst & b_idx){ +for(int i=0;i +void set(MatrixType &a, std::vectorconst & idx, const Type c){ + for(int i=0;i +void saxpy(MatrixType &a,MatrixType const &b,MatrixType const& c, std::vectorconst & a_idx, std::vectorconst & b_idx){ +for(int i=0;i +void load_problem(SpMat &A, VT &b, VT &lb, VT &ub, int &dimension, + std::string problem_name) { + { + std::string fileName(problem_name); + fileName.append(".mm"); + SpMat X; + loadMarket(X, fileName); + int m = X.rows(); + dimension = X.cols() - 1; + A = X.leftCols(dimension); + b = VT(X.col(dimension)); + } + { + std::string fileName(problem_name); + fileName.append("_bounds.mm"); + SpMat bounds; + loadMarket(bounds, fileName); + lb = VT(bounds.col(0)); + ub = VT(bounds.col(1)); + } +} #endif diff --git a/include/preprocess/crhmc/lewis_center.h b/include/preprocess/crhmc/lewis_center.h index f6ee7b22e..5bf89d151 100644 --- a/include/preprocess/crhmc/lewis_center.h +++ b/include/preprocess/crhmc/lewis_center.h @@ -28,15 +28,6 @@ #endif const size_t chol_k3 = (SIMD_LEN == 0) ? 1 : SIMD_LEN; -using NT = double; -using MT = Eigen::Matrix; -using VT = Eigen::Matrix; -using SpMat = Eigen::SparseMatrix; -using CholObj = PackedChol; -using Triple = Eigen::Triplet; -using Tx = FloatArray; -using Opts = opts; -NT epsilon = 1e-8; /*This function computes the Lewis center of the polytope*/ //And detects additional constraint that need to be added // x - It outputs the minimizer of min f(x) subjects to {Ax=b} @@ -46,9 +37,13 @@ NT epsilon = 1e-8; // because of the dom(f), the algorithm will detect the collapsed dimension // and output the detected constraint C x = d // d - detected constraint vector -template +template std::tuple lewis_center(SpMat const &A, VT const &b, Polytope &f, Opts const &options, VT x = VT::Zero(0, 1)) { + using CholObj = typename Polytope::CholObj; + using Triple = typename Polytope::Triple; + using Tx = typename Polytope::Tx; + NT epsilon = 1e-8; // initial conditions int n = A.cols(); int m = A.rows(); @@ -173,14 +168,14 @@ std::tuple lewis_center(SpMat const &A, VT const &b, Polytope std::pair pboundary = f.barrier.boundary(x); VT A_ = pboundary.first; VT b_ = pboundary.second; - A_ = A_(idx); std::vector sparseIdx; for (int i = 0; i < idx.size(); i++) { - sparseIdx.push_back(Triple(i, i, A_(i))); + sparseIdx.push_back(Triple(i, idx[i], A_(idx[i]))); } C.setFromTriplets(sparseIdx.begin(), sparseIdx.end()); - d = b_(idx); + d.resize(idx.size(), 1); + copy_indicies(d, b_, idx); } else { diff --git a/include/preprocess/crhmc/opts.h b/include/preprocess/crhmc/opts.h index df73d5a7c..573a69dbd 100644 --- a/include/preprocess/crhmc/opts.h +++ b/include/preprocess/crhmc/opts.h @@ -26,28 +26,31 @@ template class opts { const Type ipmDistanceTol = 1e-8; const Type ipmDualTol = 1e-12; int maxNZ = 30; - Type max_coord = 1e7; - bool EnableReordering = false; + Type max_coord = 1e9; + bool EnableReordering = true; const int min_convergence_steps=8; /*ODE options*/ const Type implicitTol = 1e-5; const int maxODEStep = 30; Type initialStep = 0.2; - Type convergence_limit = 1e16; + Type convergence_bound = 1e16; + + /*PackedCS Solver Options*/ Type solver_accuracy_threshold=1e-2; + int simdLen=1; /*Sampler options*/ bool DynamicWeight = true; //Enable the use of dynamic weights for each variable when sampling bool DynamicStepSize = true; // Enable adaptive step size that avoids low acceptance probability bool DynamicRegularizer = true; //Enable the addition of a regularization term - + Type regularization_factor=1e-20; /*Dynamic step choices*/ Type warmUpStep = 10; - Type maxConsecutiveBadStep = 10; + int maxConsecutiveBadStep = 10; Type targetODEStep = 10; Type shrinkFactor = 1.1; - Type minStepSize = 0.001; + Type minStepSize = 1e-5; Type effectiveStepSize = 1; opts() {} diff --git a/include/preprocess/crhmc/two_sided_barrier.h b/include/preprocess/crhmc/two_sided_barrier.h index 39b27bab9..7e279ab7f 100644 --- a/include/preprocess/crhmc/two_sided_barrier.h +++ b/include/preprocess/crhmc/two_sided_barrier.h @@ -21,6 +21,7 @@ #include "Eigen/Eigen" #include "cartesian_geom/cartesian_kernel.h" +#include "preprocess/crhmc/crhmc_utils.h" #include /// @brief A two sided barrier used by crhmc sampler @@ -43,16 +44,18 @@ template class two_sided_barrier { const NT max_step = 1e16; // largest step size const NT regularization_constant = 1e-20; // small regularization to not have a large inverse const NT unbounded_center_coord = 1e6; - VT extraHessian; //Regularization factor + MT extraHessian; const NT inf = std::numeric_limits::infinity(); - //initialization function - void set_bound(VT const &_lb, VT const &_ub) { + void set_bound(VT const &_lb, VT const &_ub) { + n = _lb.rows(); + extraHessian.resize(n, 1); + lb.resize(n); + ub.resize(n); lb = _lb; ub = _ub; - n = lb.rows(); - extraHessian = regularization_constant * VT::Ones(n); + extraHessian = regularization_constant * MT::Ones(n, 1); int x1 = 0, x2 = 0, x3 = 0; for (int i = 0; i < n; i++) { if (lb(i) == -inf) { @@ -69,35 +72,44 @@ template class two_sided_barrier { } VT c = (ub + lb) / 2; - - c(lowerIdx) = lb(lowerIdx) + VT::Ones(x2, 1) * unbounded_center_coord; - c(upperIdx) = ub(upperIdx) - VT::Ones(x1, 1) * unbounded_center_coord; - c(freeIdx) *= 0.0; + VT bias1=VT::Ones(x2, 1) * unbounded_center_coord; + saxpy(c,lb,bias1,lowerIdx,lowerIdx); + VT bias2=-VT::Ones(x1, 1) * unbounded_center_coord; + saxpy(c,ub,bias2,upperIdx,upperIdx); + set(c, freeIdx, 0.0); center = c; } two_sided_barrier(VT const &_lb, VT const &_ub, int _vdim = 1) { set_bound(_lb, _ub); vdim = _vdim; - extraHessian = regularization_constant * VT::Ones(n); + extraHessian = regularization_constant * MT::Ones(n,1); } two_sided_barrier() { vdim = 1; } - //barrier function gradient + VT gradient(VT const &x) { return (ub - x).cwiseInverse() - (x - lb).cwiseInverse(); } - //Return the barrier hessian with the extra Regularization + VT hessian(VT const &x) { VT d = ((ub - x).cwiseProduct((ub - x))).cwiseInverse() + ((x - lb).cwiseProduct((x - lb))).cwiseInverse(); return d + extraHessian; } - //third derivative of the barrier - VT tensor(VT const &x) { - VT d = 2 * (((ub - x).cwiseProduct((ub - x))).cwiseProduct((ub - x))).cwiseInverse() - - 2 * (((x - lb).cwiseProduct((x - lb))).cwiseProduct((x - lb))).cwiseInverse(); + MT hessian(MT const &x){ + MT d = (((- x).colwise()+ub).cwiseProduct(((- x).colwise()+ub))).cwiseInverse() + + ((x.colwise() - lb).cwiseProduct((x.colwise() - lb))).cwiseInverse(); + return d + extraHessian; + } + MT tensor(MT const &x) { + MT d = 2 * ((((-x).colwise()+ub).cwiseProduct(((-x).colwise()+ub))).cwiseProduct(((-x).colwise()+ub))).cwiseInverse() - + 2 * (((x.colwise() - lb).cwiseProduct(( x.colwise() - lb))).cwiseProduct(( x.colwise() - lb))).cwiseInverse(); return d; } + MT quadratic_form_gradient(MT const &x, MT const &u) { + // Output the -grad of u' (hess phi(x)) u. + return (u.cwiseProduct(u)).cwiseProduct(tensor(x)); + } VT quadratic_form_gradient(VT const &x, VT const &u) { // Output the -grad of u' (hess phi(x)) u. @@ -119,13 +131,19 @@ template class two_sided_barrier { VT boundary_distance(VT const &x) { // Output the distance of x with its closest boundary for each // coordinate - return ((x - lb).cwiseMin(ub - x)).cwiseAbs(); } bool feasible(VT const &x) { return (x.array() > lb.array() && x.array() < ub.array()).all(); } + VT feasible(MT const &x) { + VT result=VT::Ones(x.cols()); + for(int i=0;i lb.array() && x.col(i).array() < ub.array()).all(); + } + return result; + } std::pair analytic_center_oracle(VT const &x) { VT g = VT::Zero(n, 1); @@ -136,8 +154,7 @@ template class two_sided_barrier { std::pair lewis_center_oracle(VT const &x, VT const &w) { VT g = VT::Zero(n, 1); VT h = VT::Zero(n, 1); - return std::make_pair(g + w.cwiseProduct(gradient(x)), - h + w.cwiseProduct(hessian(x))); + return std::make_pair(g + w.cwiseProduct(gradient(x)),h + w.cwiseProduct(hessian(x))); } std::pair boundary(VT const &x) { @@ -145,11 +162,9 @@ template class two_sided_barrier { // Assume: only 1 vector is given VT A = VT::Ones(x.rows(), 1); - VT b = ub; b = (x.array() < center.array()).select(-lb, b); - A = (x.array() < center.array()).select(-A, A); return std::make_pair(A, b); diff --git a/include/preprocess/crhmc/weighted_two_sided_barrier.h b/include/preprocess/crhmc/weighted_two_sided_barrier.h index 1bda5d3bf..09f5eb798 100644 --- a/include/preprocess/crhmc/weighted_two_sided_barrier.h +++ b/include/preprocess/crhmc/weighted_two_sided_barrier.h @@ -21,6 +21,7 @@ #include "Eigen/Eigen" #include "cartesian_geom/cartesian_kernel.h" +#include "preprocess/crhmc/crhmc_utils.h" #include /// @brief A weighted two sided barrier used by crhmc sampler @@ -43,8 +44,7 @@ template class weighted_two_sided_barrier { const NT max_step = 1e16; // largest step size const NT regularization_constant = 1e-20; // small regularization to not have a large inverse const NT unbounded_center_coord = 1e6; - VT extraHessian; - + MT extraHessian; const NT inf = std::numeric_limits::infinity(); VT w; @@ -54,7 +54,7 @@ template class weighted_two_sided_barrier { set_bound(_lb, _ub); w = _w; vdim = _vdim; - extraHessian = regularization_constant * VT::Ones(n); + extraHessian = regularization_constant * VT::Ones(n,1); } weighted_two_sided_barrier() { vdim = 1; } @@ -67,11 +67,25 @@ template class weighted_two_sided_barrier { w.cwiseQuotient((x - lb).cwiseProduct((x - lb))); return d + extraHessian; } + MT hessian(MT const &x){ + MT d = (((- x).colwise()+ub).cwiseProduct(((- x).colwise()+ub))).cwiseInverse() + + ((x.colwise() - lb).cwiseProduct((x.colwise() - lb))).cwiseInverse(); + return w.asDiagonal()*d + extraHessian; + } VT tensor(VT const &x) { VT d = 2 * w.cwiseQuotient(((ub - x).cwiseProduct((ub - x))).cwiseProduct((ub - x))) - 2 * w.cwiseQuotient(((x - lb).cwiseProduct((x - lb))).cwiseProduct((x - lb))); return d; } + MT tensor(MT const &x) { + MT d = 2 * ((((-x).colwise()+ub).cwiseProduct(((-x).colwise()+ub))).cwiseProduct(((-x).colwise()+ub))).cwiseInverse() - + 2 * (((x.colwise() - lb).cwiseProduct(( x.colwise() - lb))).cwiseProduct(( x.colwise() - lb))).cwiseInverse(); + return w.asDiagonal()*d; + } + MT quadratic_form_gradient(MT const &x, MT const &u) { + // Output the -grad of u' (hess phi(x)) u. + return (u.cwiseProduct(u)).cwiseProduct(tensor(x)); + } VT quadratic_form_gradient(VT const &x, VT const &u) { // Output the -grad of u' (hess phi(x)) u. @@ -99,7 +113,13 @@ template class weighted_two_sided_barrier { bool feasible(VT const &x) { return (x.array() > lb.array() && x.array() < ub.array()).all(); } - + VT feasible(MT const &x) { + VT result=VT::Ones(x.cols()); + for(int i=0;i lb.array() && x.col(i).array() < ub.array()).all(); + } + return result; + } void set_bound(VT const &_lb, VT const &_ub) { lb = _lb; @@ -122,10 +142,11 @@ template class weighted_two_sided_barrier { } VT c = (ub + lb) / 2; - - c(lowerIdx) = lb(lowerIdx) + VT::Ones(x2, 1) * unbounded_center_coord; - c(upperIdx) = ub(upperIdx) - VT::Ones(x1, 1) * unbounded_center_coord; - c(freeIdx) *= 0.0; + VT bias1=VT::Ones(x2, 1) * unbounded_center_coord; + saxpy(c,lb,bias1,lowerIdx,lowerIdx); + VT bias2=-VT::Ones(x1, 1) * unbounded_center_coord; + saxpy(c,ub,bias2,upperIdx,upperIdx); + set(c, freeIdx, 0.0); center = c; } @@ -133,13 +154,10 @@ template class weighted_two_sided_barrier { std::pair boundary(VT const &x) { // Output the normal at the boundary around x for each barrier. // Assume: only 1 vector is given - VT A = VT::Ones(x.rows(), 1); - VT b = ub; b = (x.array() < center.array()).select(-lb, b); - A = (x.array() < center.array()).select(-A, A); return std::make_pair(A, b); diff --git a/include/random_walks/crhmc/additional_units/auto_tuner.hpp b/include/random_walks/crhmc/additional_units/auto_tuner.hpp index 0fc50e79a..66a8d51a7 100644 --- a/include/random_walks/crhmc/additional_units/auto_tuner.hpp +++ b/include/random_walks/crhmc/additional_units/auto_tuner.hpp @@ -33,24 +33,23 @@ class auto_tuner { public: Opts options; - weight_tuner *tune_weights; - regularizion_tuner *tune_regularization; - step_size_tuner *tune_step_size; + std::unique_ptr tune_weights; + std::unique_ptr tune_regularization; + std::unique_ptr tune_step_size; auto_tuner(Sampler &s) : options(s.params.options) { if (options.DynamicWeight) { - tune_weights = new weight_tuner(s); + tune_weights = std::unique_ptr(new weight_tuner(s)); } if (options.DynamicRegularizer) { - tune_regularization = new regularizion_tuner(s); + tune_regularization = std::unique_ptr(new regularizion_tuner(s)); } if (options.DynamicStepSize) { - tune_step_size = new step_size_tuner(s); + tune_step_size = std::unique_ptr(new step_size_tuner(s)); } } - void updateModules(Sampler &s, RandomNumberGenerator &rng) - { + void updateModules(Sampler &s, RandomNumberGenerator &rng) { if (options.DynamicWeight) { tune_weights->update_weights(s, rng); } diff --git a/include/random_walks/crhmc/additional_units/dynamic_regularizer.hpp b/include/random_walks/crhmc/additional_units/dynamic_regularizer.hpp index 9cd80b18d..81449836b 100644 --- a/include/random_walks/crhmc/additional_units/dynamic_regularizer.hpp +++ b/include/random_walks/crhmc/additional_units/dynamic_regularizer.hpp @@ -25,33 +25,34 @@ class dynamic_regularizer { public: using NT = typename Sampler::NT; using Point = typename Sampler::point; - using VT = Eigen::Matrix; + using MT = Eigen::Matrix; using Opts = typename Sampler::Opts; int n; - VT bound; + int simdLen; + MT bound; Opts &options; - VT &extraHessian; + MT &extraHessian; dynamic_regularizer(Sampler &s) : + simdLen(s.simdLen), options(s.params.options), extraHessian(options.DynamicWeight ? s.solver->ham.weighted_barrier->extraHessian : s.solver->ham.barrier->extraHessian) { n = s.dim; - bound = VT::Ones(n); - extraHessian = VT::Ones(n); + bound = MT::Ones(n, simdLen); + extraHessian = MT::Ones(n, simdLen); } - void update_regularization_factor(Sampler &s, RandomNumberGenerator &rng) - { - VT x = s.x.getCoefficients(); - x = (x.cwiseAbs()).cwiseMax(VT::Ones(n)); + void update_regularization_factor(Sampler &s, RandomNumberGenerator &rng) { + MT x = s.x; + x = (x.cwiseAbs()).cwiseMax(1); bound = bound.cwiseMax(x); - if ((2 / (bound.array() * bound.array()) < n * extraHessian.array()).any()) { extraHessian = (0.5 / n) * (bound.cwiseProduct(bound)).cwiseInverse(); + s.solver->ham.forceUpdate = true; s.solver->ham.move({s.x, s.v}); - s.v = s.get_direction_with_momentum(n, rng, s.x, Point(n), false); + s.v = s.get_direction_with_momentum(n, rng, s.x, MT::Zero(n, simdLen), 0, false); } } }; diff --git a/include/random_walks/crhmc/additional_units/dynamic_step_size.hpp b/include/random_walks/crhmc/additional_units/dynamic_step_size.hpp index ef1871731..367211962 100644 --- a/include/random_walks/crhmc/additional_units/dynamic_step_size.hpp +++ b/include/random_walks/crhmc/additional_units/dynamic_step_size.hpp @@ -22,26 +22,34 @@ template class dynamic_step_size { using NT = typename Sampler::NT; using Opts = typename Sampler::Opts; + using IVT = Eigen::Array; + using VT = Eigen::Array; public: - int consecutiveBadStep = 0; + int simdLen; + IVT consecutiveBadStep; int iterSinceShrink = 0; - NT rejectSinceShrink = 0; + VT rejectSinceShrink; int ODEStepSinceShrink = 0; int effectiveStep = 0; bool warmupFinished = false; Opts &options; NT η NT &momentum; - NT acceptedStep = 0; + VT acceptedStep; + VT nEffectiveStep; // number of effective steps NT accumulatedMomentum = 0; - NT nEffectiveStep = 0; // number of effective steps - dynamic_step_size(Sampler &s) : + simdLen(s.simdLen), options(s.params.options), eta(s.solver->eta), momentum(s.params.momentum) { + nEffectiveStep = VT::Zero(simdLen); + acceptedStep = VT::Zero(simdLen); + consecutiveBadStep = IVT::Zero(simdLen); + rejectSinceShrink = VT::Zero(simdLen); + if (options.warmUpStep > 0) { eta = 1e-3; } else { @@ -49,53 +57,58 @@ class dynamic_step_size { } } void update_step_size(Sampler &s) { - acceptedStep = acceptedStep + s.prob; - accumulatedMomentum = s.prob * momentum * accumulatedMomentum + eta; - nEffectiveStep = nEffectiveStep + eta * accumulatedMomentum * s.accept; - - int bad_step = s.prob < 0.5 || s.solver->num_steps == options.maxODEStep ? 1 : 0; + acceptedStep = acceptedStep + s.prob.array(); + accumulatedMomentum = s.prob.mean() * momentum * accumulatedMomentum + eta; + Eigen::Matrix accept = (s.accept.template cast()); + nEffectiveStep = nEffectiveStep + eta * accumulatedMomentum * accept.array(); + IVT bad_step = IVT::Zero(simdLen); + if (s.solver->num_steps == options.maxODEStep) { + bad_step += 1; + } else { + bad_step = (s.prob.array() < 0.5).select(1, IVT::Zero(simdLen)); + } consecutiveBadStep = bad_step * consecutiveBadStep + bad_step; - NT warmupRatio = nEffectiveStep / options.warmUpStep; + NT warmupRatio = nEffectiveStep.mean() / options.warmUpStep; if (warmupRatio < 1 && !warmupFinished && - consecutiveBadStep < options.maxConsecutiveBadStep) { + consecutiveBadStep.maxCoeff() < options.maxConsecutiveBadStep) { eta = options.initialStep * std::min(warmupRatio + 1e-2, 1.0); momentum = 1 - std::min(1.0, eta / options.effectiveStepSize); return; } + if (!warmupFinished) { - acceptedStep = 0; - nEffectiveStep = 0; + acceptedStep = VT::Zero(simdLen); + nEffectiveStep = VT::Zero(simdLen); warmupFinished = true; } iterSinceShrink++; - rejectSinceShrink += 1 - s.prob; + rejectSinceShrink += 1 - s.prob.array(); ODEStepSinceShrink += s.solver->num_steps; int shrink = 0; NT shiftedIter = iterSinceShrink + 20 / (1 - momentum); NT targetProbability = std::pow((1.0 - momentum), (2 / 3)) / 4; - if (rejectSinceShrink > targetProbability * shiftedIter || - consecutiveBadStep > options.maxConsecutiveBadStep || + if (rejectSinceShrink.maxCoeff() > targetProbability * shiftedIter|| + consecutiveBadStep.maxCoeff() > options.maxConsecutiveBadStep || ODEStepSinceShrink > options.targetODEStep * shiftedIter) { shrink = 1; } if (shrink == 1) { iterSinceShrink = 0; - rejectSinceShrink = 0; ODEStepSinceShrink = 0; - consecutiveBadStep = 0; + rejectSinceShrink = VT::Zero(simdLen); + consecutiveBadStep = IVT::Zero(simdLen); eta /= options.shrinkFactor; momentum = 1 - std::min(0.999, eta / options.effectiveStepSize); if (eta < options.minStepSize) { - std::cerr << "Algorithm fails to converge even with step size h = " - << eta << "\n"; - exit(1); + s.P.terminate=true; + s.P.terminate_message="Algorithm fails to converge even with step size h = "+std::to_string(eta)+"\n"; } } diff --git a/include/random_walks/crhmc/additional_units/dynamic_weight.hpp b/include/random_walks/crhmc/additional_units/dynamic_weight.hpp index ce945216f..6b9433774 100644 --- a/include/random_walks/crhmc/additional_units/dynamic_weight.hpp +++ b/include/random_walks/crhmc/additional_units/dynamic_weight.hpp @@ -23,48 +23,52 @@ class dynamic_weight { using NT = typename Sampler::NT; using Point = typename Sampler::point; using VT = Eigen::Matrix; + using MT = Eigen::Matrix; + using IVT = Eigen::Array; using Opts = typename Sampler::Opts; public: - int consecutiveBadStep = 0; + int simdLen; + IVT consecutiveBadStep; int n; VT &w; Opts options; - dynamic_weight(Sampler &s) : - w(s.solver->ham.weighted_barrier->w), - options(s.params.options) + dynamic_weight(Sampler &s) + : simdLen(s.simdLen), w(s.solver->ham.weighted_barrier->w), options(s.params.options) { n = s.dim; + consecutiveBadStep = IVT::Zero(simdLen); } // If we have consecutive bad steps update the weights with // the help of the leverage scores. void update_weights(Sampler &s, RandomNumberGenerator &rng) { - int bad_step = 0; - NT threshold; - if (s.prob < 0.5 || s.solver->num_steps == options.maxODEStep) { - bad_step = 1; + IVT bad_step = IVT::Zero(simdLen); + if (s.solver->num_steps == options.maxODEStep) { + bad_step += 1; + } else { + bad_step = (s.prob.array() < 0.5).select(1, IVT::Zero(simdLen)); } + NT threshold; consecutiveBadStep = bad_step * consecutiveBadStep + bad_step; - if (!s.accepted) { - VT lsc = s.solver->ham.lsc; + if (s.accept.sum() < simdLen) { + VT lsc = s.solver->ham.lsc.colwise().maxCoeff().transpose(); /*The more bad steps in a row we have the higher we want the threshold to be In order to change w more drasticaly according to the leverage scores. So if we have more than 2 bad steps in a row we elect to set the threshold to 4 else to 16. Not many changes will be possible as the w should be upperbounded by 1*/ - if (consecutiveBadStep > 2) { + if (consecutiveBadStep.maxCoeff() > 2) { threshold = 4; } else { threshold = 16; } bool changed = (lsc.array() > threshold * w.array()).any(); - w = (lsc.array() > threshold * w.array()) - .select((w * threshold).cwiseMin(VT::Ones(n)), w); if (changed) { + w = (lsc.array() > threshold * w.array()).select((w * threshold).cwiseMin(1), w); s.solver->ham.forceUpdate = true; s.solver->ham.move({s.x, s.v}); - s.v = s.get_direction_with_momentum(n, rng, s.x, Point(n), false); + s.v = s.get_direction_with_momentum(n, rng, s.x, MT::Zero(n, simdLen), false); } } } diff --git a/include/random_walks/crhmc/crhmc_walk.hpp b/include/random_walks/crhmc/crhmc_walk.hpp index b97d79fa9..21ed8d6bb 100644 --- a/include/random_walks/crhmc/crhmc_walk.hpp +++ b/include/random_walks/crhmc/crhmc_walk.hpp @@ -36,7 +36,7 @@ struct CRHMCWalk { parameters(OracleFunctor const &F, unsigned int dim, Opts &user_options, - NT epsilon_ = 2) : + NT epsilon_ = 2) : options(user_options) { epsilon = epsilon_; @@ -59,17 +59,19 @@ struct CRHMCWalk { using pts = std::vector; using NT = typename Point::FT; using VT = Eigen::Matrix; + using MT = Eigen::Matrix; using Sampler = CRHMCWalk::Walk; using Opts = typename Polytope::Opts; + using IVT = Eigen::Matrix; // Hyperparameters of the sampler parameters ¶ms; // Numerical ODE solver - Solver *solver; + std::unique_ptr solver; // Dimension unsigned int dim; @@ -87,25 +89,25 @@ struct CRHMCWalk { float average_acceptance_prob = 0; // Acceptance probability - NT prob; + VT prob; bool accepted; - NT accept; + IVT accept; bool update_modules; - + int simdLen; // References to xs - Point x, v; + MT x, v; // Proposal points - Point x_tilde, v_tilde; + MT x_tilde, v_tilde; // Gradient function NegativeGradientFunctor &F; // Auto tuner - auto_tuner *module_update; + std::unique_ptr>module_update; // Helper variables - NT H, H_tilde, log_prob, u_logprob; + VT H, H_tilde; // Density exponent NegativeLogprobFunctor &f; #ifdef TIME_KEEPING @@ -118,41 +120,50 @@ struct CRHMCWalk { NegativeLogprobFunctor &neg_logprob_f, parameters ¶m) : params(param), + P(Problem), F(neg_grad_f), - f(neg_logprob_f), - P(Problem) + f(neg_logprob_f) { dim = p.dimension(); - + simdLen = params.options.simdLen; // Starting point is provided from outside - x = p; + x = p.getCoefficients() * MT::Ones(1, simdLen); accepted = false; // Initialize solver - solver = - new Solver(0.0, params.eta, pts{x, x}, F, Problem, params.options); - v = solver->get_state(1); - module_update = new auto_tuner(*this); + solver = std::unique_ptr(new Solver(0.0, params.eta, {x, x}, F, Problem, params.options)); + v = MT::Zero(dim, simdLen); + module_update = std::unique_ptr>(new auto_tuner(*this)); update_modules = params.options.DynamicWeight || params.options.DynamicRegularizer || params.options.DynamicStepSize; }; - Point get_direction_with_momentum(unsigned int const &dim, - RandomNumberGenerator &rng, - Point x, - Point v, - NT momentum = 0, - bool normalize = true) + // Sample a new velocity with momentum + MT get_direction_with_momentum(unsigned int const &dim, + RandomNumberGenerator &rng, MT const &x, MT v, + NT momentum = 0, bool normalize = true) { - Point z = GetDirection::apply(dim, rng, normalize); + MT z = MT(dim, simdLen); + for (int i = 0; i < simdLen; i++) + { + z.col(i) = GetDirection::apply(dim, rng, normalize).getCoefficients(); + } solver->ham.move({x, v}); - VT sqrthess = (solver->ham.hess).cwiseSqrt(); - z = Point(sqrthess.cwiseProduct(z.getCoefficients())); + MT sqrthess = (solver->ham.hess).cwiseSqrt(); + z = sqrthess.cwiseProduct(z); return v * std::sqrt(momentum) + z * std::sqrt(1 - momentum); } // Returns the current point in the tranformed in the original space - inline Point getPoint() { return Point(P.T * x.getCoefficients() + P.y); } + inline MT getPoints() { return (P.T * x).colwise() + P.y; } + // Returns the current point in the tranformed in the original space + inline Point getPoint() { return Point(P.T * x.col(0) + P.y); } + inline MT masked_choose(MT &x, MT &x_tilde, IVT &accept) { + return accept.transpose().replicate(x.rows(), 1).select(x_tilde, x); + } + inline void disable_adaptive(){ + update_modules=false; + } inline void apply(RandomNumberGenerator &rng, int walk_length = 1, bool metropolis_filter = true) @@ -160,47 +171,45 @@ struct CRHMCWalk { num_runs++; // Pick a random velocity with momentum v = get_direction_with_momentum(dim, rng, x, v, params.momentum, false); - solver->set_state(0, x); solver->set_state(1, v); // Get proposals solver->steps(walk_length, accepted); x_tilde = solver->get_state(0); v_tilde = solver->get_state(1); - if (metropolis_filter) { #ifdef TIME_KEEPING start = std::chrono::system_clock::now(); #endif // Calculate initial Hamiltonian H = solver->ham.hamiltonian(x, v); + // Calculate new Hamiltonian - H_tilde = solver->ham.hamiltonian(x_tilde, Point(dim) - v_tilde); + H_tilde = solver->ham.hamiltonian(x_tilde, -v_tilde); + #ifdef TIME_KEEPING end = std::chrono::system_clock::now(); H_duration += end - start; #endif - NT feasible = solver->ham.feasible(x_tilde.getCoefficients(), - v_tilde.getCoefficients()); - prob = std::min(1.0, exp(H - H_tilde)) * feasible; - - log_prob = log(prob); - total_acceptance_prob += prob; - - // Decide to switch - if (rng.sample_urdist() < prob) { - x = x_tilde; - v = v_tilde; - accepted = true; - } - else { - total_discarded_samples++; - accepted = false; - v = Point(dim) - v; + VT feasible = solver->ham.feasible(x_tilde, + v_tilde); + prob = (1.0 < exp((H - H_tilde).array())).select(1.0, exp((H - H_tilde).array())); + prob = (feasible.array() > 0.5).select(prob, 0); + + total_acceptance_prob += prob.sum(); + VT rng_vector = VT(simdLen); + for (int i = 0; i < simdLen; i++) + { + rng_vector(i) = rng.sample_urdist(); } - discard_ratio = (1.0 * total_discarded_samples) / num_runs; - average_acceptance_prob = total_acceptance_prob / num_runs; - accept = accepted ? 1 : 0; + accept = (rng_vector.array() < prob.array()).select(1 * IVT::Ones(simdLen), 0 * IVT::Ones(simdLen)); + + x = masked_choose(x, x_tilde, accept); + v = -v; + v = masked_choose(v, v_tilde, accept); + total_discarded_samples += simdLen - accept.sum(); + discard_ratio = (1.0 * total_discarded_samples) / (num_runs * simdLen); + average_acceptance_prob = total_acceptance_prob / (num_runs * simdLen); } else { x = x_tilde; v = v_tilde; @@ -210,18 +219,24 @@ struct CRHMCWalk { } } #ifdef TIME_KEEPING - void print_timing_information() { - std::cerr << "--------------Timing Information--------------\n"; + void initialize_timers() { + H_duration = std::chrono::duration::zero(); + solver->DU_duration = std::chrono::duration::zero(); + solver->approxDK_duration = std::chrono::duration::zero(); + } + template + void print_timing_information(StreamType &stream) { + stream << "---Sampling Timing Information" << std::endl; double DU_time = solver->DU_duration.count(); double DK_time = solver->approxDK_duration.count(); double H_time = H_duration.count(); double total_time = H_time + DK_time + DU_time; - std::cerr << "Computing the Hamiltonian in time, " << H_time << " secs\n"; - std::cerr << "Computing DU partial derivatives in time, " << DU_time - << " secs\n"; - std::cerr << "Computing DK partial derivatives in time, " << DK_time - << " secs\n"; - std::cerr << "H_time + DK_time + DU_time: " << total_time << "\n"; + stream << "Computing the Hamiltonian in time, " << H_time << " secs\n"; + stream << "Computing DU partial derivatives in time, " << DU_time + << " secs\n"; + stream << "Computing DK partial derivatives in time, " << DK_time + << " secs\n"; + stream << "H_time + DK_time + DU_time: " << total_time << "\n"; } #endif }; diff --git a/include/random_walks/crhmc/hamiltonian.hpp b/include/random_walks/crhmc/hamiltonian.hpp index d8cb5a82e..0d9ef5465 100644 --- a/include/random_walks/crhmc/hamiltonian.hpp +++ b/include/random_walks/crhmc/hamiltonian.hpp @@ -17,42 +17,43 @@ #define HAMILTONIAN_HPP #include "preprocess/crhmc/two_sided_barrier.h" #include "preprocess/crhmc/weighted_two_sided_barrier.h" +#include "PackedCSparse/PackedChol.h" #include "preprocess/crhmc/crhmc_utils.h" #include -/// @brief Class for the hamiltonian used in crhmc sampler -/// @tparam Polytope Polytope Type -/// @tparam Point Point Type -template +template class Hamiltonian { using VT = typename Polytope::VT; + using IVT = Eigen::Array; + using BVT = Eigen::Matrix; using NT = typename Polytope::NT; using MT = typename Polytope::MT; - using Tx = typename Polytope::Tx; - using CholObj = typename Polytope::CholObj; - using Opts = typename Polytope::Opts; using SpMat = typename Polytope::SpMat; - using pts = std::vector; + using Tx = FloatArray; + using CholObj = PackedChol; + using Opts = typename Polytope::Opts; + using pts = std::vector; using Barrier = two_sided_barrier; using WeightedBarrier = weighted_two_sided_barrier; public: bool prepared = false; - bool forceUpdate = true; + bool forceUpdate = true; // Update function oracle temporary varibles Polytope &P; - VT hess; + MT hess; bool dUDx_empty = true; - Point last_dUdx; + MT last_dUdx; CholObj solver; pts xs; - VT x; - VT dfx; - VT lsc; - NT fx = 0; + MT x; + MT dfx; + MT lsc; + VT fx; int n; int m; + int num_runs = 0; Barrier *barrier; - WeightedBarrier *weighted_barrier; + std::unique_ptr weighted_barrier; Opts &options; Hamiltonian(Polytope &boundaries) : P(boundaries), @@ -61,111 +62,122 @@ class Hamiltonian { { n = P.dimension(); m = P.equations(); - x = VT::Zero(n); - xs = {Point(n), Point(n)}; - lsc = VT::Zero(n); + x = MT::Zero(n, simdLen); + xs = {x, x}; + lsc = MT::Zero(simdLen, n); solver.accuracyThreshold = options.solver_accuracy_threshold; - if (options.DynamicWeight) { + if (options.DynamicWeight) + { weighted_barrier = - new WeightedBarrier(P.barrier.lb, P.barrier.ub, P.w_center); + std::unique_ptr(new WeightedBarrier(P.barrier.lb, P.barrier.ub, P.w_center)); + weighted_barrier->extraHessian.resize(n, simdLen); + weighted_barrier->extraHessian = MT::Ones(n, simdLen) * options.regularization_factor; } barrier = &P.barrier; + barrier->extraHessian.resize(n, simdLen); + barrier->extraHessian = MT::Ones(n, simdLen) * options.regularization_factor; } // Compute H(x,v) - NT hamiltonian(Point x, Point v) + VT hamiltonian(MT x, MT v) { prepare({x, v}); pts pd = DK({x, v}); - NT K = 0.5 * v.dot(pd[0]); - NT U = 0.5 * (solver.logdet() + ((hess.array()).log()).sum()); - U = U + fx; - NT E = U + K; + VT K = 0.5 * (v.cwiseProduct(pd[0])).colwise().sum(); + Tx out=solver.logdet(); + VT logdet=VT(simdLen); + for (int i = 0; i < simdLen; i++) + logdet(i) = get(out, i); + VT U = ((hess.array()).log()).colwise().sum(); + U = (U + logdet) * 0.5 + fx; + VT E = U + K; return E; } // Helper is nan function for vectors template - bool isnan(MatrixType x) + IVT is_not_nan(MatrixType x) { + IVT result = IVT::Ones(x.cols()); for (int i = 0; i < x.rows(); i++) { for (int j = 0; j < x.cols(); j++) { if (std::isnan(x(i, j))) { - return true; + result(j) = 0; } } } - return false; + return result; } // Test if the values of x and v are valid and if x is feasible - NT feasible(VT x, VT v) + VT feasible(MT x, MT v) { - bool feasible_coordinate = true; + VT feasible_coordinate = VT::Ones(x.cols()); if (options.DynamicWeight) { feasible_coordinate = weighted_barrier->feasible(x); } else { feasible_coordinate = barrier->feasible(x); } - bool r = !isnan(x) && !isnan(v) && feasible_coordinate; - if (r) { - return 1; - } - return 0; + VT r = feasible_coordinate.cwiseProduct((is_not_nan(x) * is_not_nan(v)).matrix()); + return r; } // prepare the solver weighted by the hessian void prepare(pts const &xs) { move(xs); if (!prepared) { - VT Hinv = hess.cwiseInverse(); + MT Hinv = (hess.cwiseInverse()).transpose(); solver.decompose((Tx *)Hinv.data()); + dUDx_empty = true; } - dUDx_empty = true; prepared = true; } // Computation of the partial derivatives of the K term pts DK(pts const &x_bar) { - VT x = x_bar[0].getCoefficients(); - VT v = x_bar[1].getCoefficients(); + MT x = x_bar[0]; + MT v = x_bar[1]; move(x_bar); - VT invHessV = v.cwiseQuotient(hess); - VT input_vector = P.Asp * invHessV; - VT out_vector = VT::Zero(m); + MT invHessV = v.cwiseQuotient(hess); + MT input_vector = P.Asp * invHessV; + input_vector.transposeInPlace(); + MT out_vector = MT::Zero(simdLen, m); solver.solve((Tx *)input_vector.data(), (Tx *)out_vector.data()); - Point dKdv = - Point(invHessV - (P.Asp.transpose() * out_vector).cwiseQuotient(hess)); + out_vector.transposeInPlace(); + MT dKdv = + invHessV - (P.Asp.transpose() * out_vector).cwiseQuotient(hess); - Point dKdx = Point(n); + MT dKdx = MT::Zero(n, simdLen); if (options.DynamicWeight) { - dKdx = Point( - weighted_barrier->quadratic_form_gradient(x, dKdv.getCoefficients()) / - 2); + dKdx = + weighted_barrier->quadratic_form_gradient(x, dKdv) / + 2; } else { - dKdx = Point(barrier->quadratic_form_gradient(x, dKdv.getCoefficients()) / - 2); + dKdx = barrier->quadratic_form_gradient(x, dKdv) / + 2; } return {dKdv, dKdx}; } // Approximate computation of the partial derivatives of the K term - pts approxDK(pts const &x_bar, VT &nu) + pts approxDK(pts const &x_bar, MT &nu) { - VT x = x_bar[0].getCoefficients(); - VT v = x_bar[1].getCoefficients(); + MT x = x_bar[0]; + MT v = x_bar[1]; move(x_bar); - VT dUdv_b = P.Asp * (v - P.Asp.transpose() * nu).cwiseQuotient(hess); - VT out_solver = VT(nu.rows(), nu.cols()); + MT dUdv_b = P.Asp * (v - P.Asp.transpose() * nu).cwiseQuotient(hess); + dUdv_b.transposeInPlace(); + MT out_solver = MT(nu.cols(), nu.rows()); solver.solve((Tx *)dUdv_b.data(), (Tx *)out_solver.data()); - nu = nu + out_solver; - Point dKdv = Point((v - P.Asp.transpose() * nu).cwiseQuotient(hess)); - Point dKdx = Point(n); + nu = nu + out_solver.transpose(); + + MT dKdv = (v - P.Asp.transpose() * nu).cwiseQuotient(hess); + MT dKdx = MT::Zero(n, simdLen); if (options.DynamicWeight) { - dKdx = Point( - weighted_barrier->quadratic_form_gradient(x, dKdv.getCoefficients()) / - 2); + dKdx = + weighted_barrier->quadratic_form_gradient(x, dKdv) / + 2; } else { - dKdx = Point(barrier->quadratic_form_gradient(x, dKdv.getCoefficients()) / - 2); + dKdx = barrier->quadratic_form_gradient(x, dKdv) / + 2; } return {dKdv, dKdx}; } @@ -173,24 +185,25 @@ class Hamiltonian { // This is only dependent on x and so DU/Dv=0 pts DU(pts const &x_bar) { - VT x = x_bar[0].getCoefficients(); + MT x = x_bar[0]; move(x_bar); if (!prepared || dUDx_empty) { prepare(x_bar); solver.leverageScoreComplement((Tx *)lsc.data()); if (options.DynamicWeight) { - last_dUdx = Point(-(weighted_barrier->tensor(x).cwiseProduct(lsc)) - .cwiseQuotient(2 * hess) - - dfx); + last_dUdx = (weighted_barrier->tensor(x).cwiseProduct(lsc.transpose())) + .cwiseQuotient(2 * hess) + + dfx; } else { - last_dUdx = Point( - -(barrier->tensor(x).cwiseProduct(lsc)).cwiseQuotient(2 * hess) - - dfx); + last_dUdx = + (barrier->tensor(x).cwiseProduct(lsc.transpose())).cwiseQuotient(2 * hess) + + dfx; } dUDx_empty = false; } - return {Point(n), last_dUdx}; + + return {MT::Zero(n, simdLen), -last_dUdx}; } // Compute the computations involving only x iff x has been changed // Else they are stored @@ -200,37 +213,37 @@ class Hamiltonian { return; } xs = y; - x = xs[0].getCoefficients(); - VT h; + x = xs[0]; + MT h; std::tie(fx, dfx, h) = P.f_oracle(x); if (options.DynamicWeight) { hess = weighted_barrier->hessian(x) + h; } else { hess = barrier->hessian(x) + h; } - forceUpdate = false; prepared = false; } // Project x to the polytope - void project(pts &xs) - { + void project(pts &xs) { move(xs); - VT x = xs[0].getCoefficients(); + MT x = xs[0]; int m = P.Asp.rows(); - VT out_vector = VT(m); - VT in_vector = P.b - P.Asp * x; + MT out_vector = MT(simdLen, m); + MT in_vector = (-P.Asp * x).colwise() + P.b; + in_vector.transposeInPlace(); solver.solve((Tx *)in_vector.data(), (Tx *)out_vector.data()); + out_vector.transposeInPlace(); out_vector = P.Asp.transpose() * out_vector; - xs[0] = xs[0] + Point((out_vector).cwiseQuotient(hess)); + xs[0] = xs[0] + (out_vector).cwiseQuotient(hess); } // Get the inner product of x and ds weighted by the hessian - NT x_norm(pts const &xs, pts const &dx) + VT x_norm(pts const &xs, pts const &dx) { move(xs); - VT dx_x = dx[0].getCoefficients(); - VT r = (dx_x.cwiseProduct(dx_x)).cwiseProduct(hess); - return r.sum(); + MT dx_x = dx[0]; + MT r = (dx_x.cwiseProduct(dx_x)).cwiseProduct(hess); + return r.colwise().sum(); } }; #endif diff --git a/include/sampling/random_point_generators.hpp b/include/sampling/random_point_generators.hpp index a2a811dd1..e70e14617 100644 --- a/include/sampling/random_point_generators.hpp +++ b/include/sampling/random_point_generators.hpp @@ -255,6 +255,65 @@ struct LogconcaveRandomPointGenerator } }; +template +< + typename Walk +> +struct CrhmcRandomPointGenerator +{ + + template + < + typename Polytope, + typename Point, + typename PointList, + typename WalkPolicy, + typename RandomNumberGenerator, + typename NegativeGradientFunctor, + typename NegativeLogprobFunctor, + typename Parameters + > + static void apply(Polytope &P, + Point &p, // a point to start + unsigned int const& rnum, + unsigned int const& walk_length, + PointList &randPoints, + WalkPolicy &policy, + RandomNumberGenerator &rng, + NegativeGradientFunctor &F, + NegativeLogprobFunctor &f, + Parameters ¶meters, + Walk &walk, + int simdLen=1, + bool raw_output= false) + { + typedef typename Walk::MT MT; + for (unsigned int i = 0; i < std::ceil((float)rnum/simdLen); ++i) + { + // Gather one sample + walk.apply(rng, walk_length); + if(walk.P.terminate){return;} + MT x; + if(raw_output){ + x=walk.x; + }else{ + x=walk.getPoints(); + } + if((i + 1) * simdLen > rnum){ + for(int j = 0; j < rnum-simdLen*i; j++){ + Point p = Point(x.col(j)); + policy.apply(randPoints, p); + } + break; + } + // Use PushBackWalkPolicy + for(int j=0; j +void crhmc_sampling(PointList &randPoints, + Polytope &P, + RandomNumberGenerator &rng, + const int walk_len, + const unsigned int rnum, + const unsigned int nburns, + NegativeGradientFunctor &F, + NegativeLogprobFunctor &f, + HessianFunctor &h, + int simdLen = 1, + bool raw_output=false) { + typedef typename Polytope::MT MatrixType; + typedef crhmc_input + < + MatrixType, + Point, + NegativeLogprobFunctor, + NegativeGradientFunctor, + HessianFunctor + > Input; + Input input = convert2crhmc_input(P, f, F, h); + typedef crhmc_problem CrhmcProblem; + CrhmcProblem problem = CrhmcProblem(input); + if(problem.terminate){return;} + typedef typename WalkTypePolicy::template Walk + < + Point, + CrhmcProblem, + RandomNumberGenerator, + NegativeGradientFunctor, + NegativeLogprobFunctor, + Solver + > walk; + typedef typename WalkTypePolicy::template parameters + < + NT, + NegativeGradientFunctor + > walk_params; + Point p = Point(problem.center); + problem.options.simdLen=simdLen; + walk_params params(input.df, p.dimension(), problem.options); + + if (input.df.params.eta > 0) { + params.eta = input.df.params.eta; + } + + PushBackWalkPolicy push_back_policy; + + walk crhmc_walk = walk(problem, p, input.df, input.f, params); + + typedef CrhmcRandomPointGenerator RandomPointGenerator; + + RandomPointGenerator::apply(problem, p, nburns, walk_len, randPoints, + push_back_policy, rng, F, f, params, crhmc_walk); + //crhmc_walk.disable_adaptive(); + randPoints.clear(); + RandomPointGenerator::apply(problem, p, rnum, walk_len, randPoints, + push_back_policy, rng, F, f, params, crhmc_walk, simdLen, raw_output); +} +#include "ode_solvers/ode_solvers.hpp" +template < + typename Polytope, + typename RNGType, + typename PointList, + typename NegativeGradientFunctor, + typename NegativeLogprobFunctor, + typename HessianFunctor, + typename CRHMCWalk, + int simdLen=1 +> +void execute_crhmc(Polytope &P, RNGType &rng, PointList &randPoints, + unsigned int const& walkL, unsigned int const& numpoints, + unsigned int const& nburns, NegativeGradientFunctor *F=NULL, + NegativeLogprobFunctor *f=NULL, HessianFunctor *h=NULL, bool raw_output= false){ +typedef typename Polytope::MT MatrixType; +typedef typename Polytope::PointType Point; +typedef typename Point::FT NT; +if(h!=NULL){ +typedef crhmc_input + < + MatrixType, + Point, + NegativeLogprobFunctor, + NegativeGradientFunctor, + HessianFunctor + > Input; +typedef crhmc_problem CrhmcProblem; +crhmc_sampling < + PointList, + Polytope, + RNGType, + CRHMCWalk, + NT, + Point, + NegativeGradientFunctor, + NegativeLogprobFunctor, + HessianFunctor, + ImplicitMidpointODESolver < + Point, + NT, + CrhmcProblem, + NegativeGradientFunctor, + simdLen + > +>(randPoints, P, rng, walkL, numpoints, nburns, *F, *f, *h, simdLen, raw_output); +}else{ + typedef crhmc_input + < + MatrixType, + Point, + NegativeLogprobFunctor, + NegativeGradientFunctor, + ZeroFunctor + > Input; + typedef crhmc_problem CrhmcProblem; + ZeroFunctor zerof; +crhmc_sampling < + PointList, + Polytope, + RNGType, + CRHMCWalk, + NT, + Point, + NegativeGradientFunctor, + NegativeLogprobFunctor, + ZeroFunctor, + ImplicitMidpointODESolver < + Point, + NT, + CrhmcProblem, + NegativeGradientFunctor, + simdLen + > +>(randPoints, P, rng, walkL, numpoints, nburns, *F, *f, zerof, simdLen, raw_output); +} +} template < typename WalkTypePolicy, diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index d95a49c7f..3adbe4681 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -324,12 +324,14 @@ else () COMMAND logconcave_sampling_test -tc=benchmark_nuts_hmc) + add_executable (crhmc_sampling_test crhmc_sampling_test.cpp $) add_test(NAME crhmc_sampling_test_crhmc COMMAND crhmc_sampling_test -tc=crhmc) add_test(NAME crhmc_test_polytope_sampling COMMAND crhmc_sampling_test -tc=test_polytope_sampling_crhmc) - + add_test(NAME crhmc_test_sparse_sampling + COMMAND crhmc_sampling_test -tc=test_sampling_sparse_problem) add_executable (simple_mc_integration simple_mc_integration.cpp $) add_test(NAME simple_mc_integration_over_limits COMMAND simple_mc_integration -tc=rectangle) @@ -365,7 +367,7 @@ else () #set_target_properties(benchmarks_crhmc # PROPERTIES COMPILE_FLAGS ${ADDITIONAL_FLAGS}) #set_target_properties(benchmarks_crhmc_sampling - # PROPERTIES COMPILE_FLAGS ${ADDITIONAL_FLAGS}) + # PROPERTIES COMPILE_FLAGS ${ADDITIONAL_FLAGS}) set_target_properties(crhmc_polytope_preparation_test PROPERTIES COMPILE_FLAGS ${ADDITIONAL_FLAGS}) set_target_properties(crhmc_sampling_test @@ -389,15 +391,15 @@ else () TARGET_LINK_LIBRARIES(benchmarks_sob ${LP_SOLVE} ${MKL_LINK} coverage_config) TARGET_LINK_LIBRARIES(benchmarks_cg ${LP_SOLVE} ${MKL_LINK} coverage_config) TARGET_LINK_LIBRARIES(benchmarks_cb ${LP_SOLVE} ${MKL_LINK} coverage_config) - #TARGET_LINK_LIBRARIES(benchmarks_crhmc_sampling ${LP_SOLVE} ${MKL_LINK} ${QD_LIB} coverage_config) - #TARGET_LINK_LIBRARIES(benchmarks_crhmc ${LP_SOLVE} ${MKL_LINK} ${QD_LIB} coverage_config) + #TARGET_LINK_LIBRARIES(benchmarks_crhmc_sampling ${LP_SOLVE} ${MKL_LINK} QD_LIB coverage_config) + #TARGET_LINK_LIBRARIES(benchmarks_crhmc ${LP_SOLVE} ${MKL_LINK} QD_LIB coverage_config) TARGET_LINK_LIBRARIES(simple_mc_integration ${LP_SOLVE} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(ode_solvers_test ${LP_SOLVE} ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} ${QD_LIB} coverage_config) + TARGET_LINK_LIBRARIES(ode_solvers_test ${LP_SOLVE} ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} QD_LIB coverage_config) TARGET_LINK_LIBRARIES(boundary_oracles_test ${LP_SOLVE} ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} coverage_config) TARGET_LINK_LIBRARIES(root_finders_test ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(crhmc_polytope_preparation_test ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} ${QD_LIB} coverage_config) + TARGET_LINK_LIBRARIES(crhmc_polytope_preparation_test ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} QD_LIB coverage_config) TARGET_LINK_LIBRARIES(logconcave_sampling_test ${LP_SOLVE} ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(crhmc_sampling_test ${LP_SOLVE} ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} ${QD_LIB} coverage_config) + TARGET_LINK_LIBRARIES(crhmc_sampling_test ${LP_SOLVE} ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} QD_LIB coverage_config) TARGET_LINK_LIBRARIES(order_polytope ${LP_SOLVE} coverage_config) TARGET_LINK_LIBRARIES(matrix_sampling_test ${LP_SOLVE} ${MKL_LINK} coverage_config) diff --git a/test/benchmarks_crhmc.cpp b/test/benchmarks_crhmc.cpp index 848c4d6db..3088cb656 100644 --- a/test/benchmarks_crhmc.cpp +++ b/test/benchmarks_crhmc.cpp @@ -24,12 +24,13 @@ #include #include using NT = double; +using VT = Eigen::Matrix; +using MT = Eigen::Matrix; using Kernel = Cartesian; using Point = typename Kernel::Point; using Hpolytope = HPolytope; using Input = crhmc_input; using CrhmcProblem = crhmc_problem; -using VT = Eigen::Matrix; inline bool exists_check(const std::string &name) { std::ifstream f(name.c_str()); diff --git a/test/benchmarks_crhmc_sampling.cpp b/test/benchmarks_crhmc_sampling.cpp index 27460b5be..6706553a7 100644 --- a/test/benchmarks_crhmc_sampling.cpp +++ b/test/benchmarks_crhmc_sampling.cpp @@ -51,8 +51,7 @@ struct SimulationStats { NT average_acceptance_prob = NT(0); NT step_size = NT(0); - friend std::ostream &operator<<(std::ostream &out, - const SimulationStats &stats) + friend std::ostream &operator<<(std::ostream &out, const SimulationStats &stats) { out << stats.method << "," << stats.walk_length << "," << stats.min_ess << "," << stats.max_psrf << "," << stats.time_per_draw << "," @@ -147,13 +146,12 @@ NT check_interval_psrf(MT &samples, NT target = NT(1.2)) { } return max_psrf; } -template -std::vector> benchmark_polytope_sampling( - Polytope &P, NT eta = NT(-1), unsigned int walk_length = 1, - double target_time = std::numeric_limits::max(), bool rounding = false, - bool centered = false, unsigned int max_draws = 80000, - unsigned int num_burns = 20000) -{ +template +std::vector> benchmark_polytope_sampling(StreamType &stream, + Polytope &P, NT eta = NT(-1), unsigned int walk_length = 1, + double target_time = std::numeric_limits::max(), bool rounding = false, + bool centered = false, unsigned int max_draws = 80000, + unsigned int num_burns = 20000) { using Kernel = Cartesian; using Point = typename Kernel::Point; using MT = typename Polytope::MT; @@ -164,7 +162,7 @@ std::vector> benchmark_polytope_sampling( using Input = crhmc_input; using CrhmcProblem = crhmc_problem; using Opts = opts; - using Solver = ImplicitMidpointODESolver; + using Solver = ImplicitMidpointODESolver; using RandomNumberGenerator = BoostRandomNumberGenerator; SimulationStats rdhr_stats; @@ -187,7 +185,7 @@ std::vector> benchmark_polytope_sampling( unsigned int dim = x0.dimension(); if (rounding) { - std::cout << "SVD Rounding" << std::endl; + stream << "SVD Rounding" << std::endl; svd_rounding(P, inner_ball, walk_length, rng); } @@ -207,12 +205,17 @@ std::vector> benchmark_polytope_sampling( std::chrono::time_point start, stop; Opts options; + options.simdLen = simdLen; CRHMCWalk::parameters crhmc_params(F, dim, options); Input input = Input(P.dimension(), f, F, H); input.Aineq = P.get_mat(); input.bineq = P.get_vec(); CrhmcProblem crhmc_problem = CrhmcProblem(input); +#ifdef TIME_KEEPING + crhmc_problem.print_preparation_time(stream); +#endif + Point x_start(crhmc_problem.center); CRHMCWalk::Walk @@ -237,13 +240,20 @@ std::vector> benchmark_polytope_sampling( std::cout << std::endl; std::cout << "Sampling" << std::endl; - +#ifdef TIME_KEEPING + crhmc.initialize_timers(); +#endif start = std::chrono::high_resolution_clock::now(); - for (unsigned int i = 0; i < max_actual_draws; i++) { + for (unsigned int i = 0; i < std::ceil(max_actual_draws / simdLen); i++) { for (int k = 0; k < walk_length; k++) { crhmc.apply(rng, 1); } - samples.col(i) = crhmc.getPoint().getCoefficients(); + MT sample = crhmc.getPoints(); + if (i * simdLen + simdLen - 1 < max_actual_draws) { + samples(Eigen::all, Eigen::seq(i * simdLen, i * simdLen + simdLen - 1)) = sample; + } else { + samples(Eigen::all, Eigen::seq(i * simdLen, max_actual_draws - 1)) = sample(Eigen::all, Eigen::seq(0, max_actual_draws - 1 - simdLen * i)); + } if (i % 1000 == 0 && i > 0) std::cout << "."; } @@ -251,30 +261,31 @@ std::vector> benchmark_polytope_sampling( ETA = (NT)std::chrono::duration_cast(stop - start) .count(); - std::cout << std::endl; -#ifdef TIME_KEEPING std::chrono::duration total_time = stop - start; - std::cerr << "Total time: " << total_time.count() << "\n"; + stream << "Total Sampling time: " << total_time.count() << "\n"; assert(total_time.count() < target_time); - std::cout << "Assertion (preparation_time< " << target_time - << " secs) passed!" << std::endl - << std::endl; - crhmc.print_timing_information(); + stream << "Assertion (preparation_time< " << target_time + << " secs) passed!" << std::endl + << std::endl; +#ifdef TIME_KEEPING + crhmc.print_timing_information(stream); #endif print_diagnostics(samples, min_ess, std::cout); - std::cout << "min ess " << min_ess << "us" << std::endl; - std::cout << "Average time per sample: " << ETA / max_actual_draws << "us" - << std::endl; - std::cout << "Average time per independent sample: " << ETA / min_ess << "us" - << std::endl; - std::cout << "Step size (final): " << crhmc.solver->eta << std::endl; - std::cout << "Discard Ratio: " << crhmc.discard_ratio << std::endl; - std::cout << "Average Acceptance Probability: " - << crhmc.average_acceptance_prob << std::endl; + stream << "Problem original size: m= " << P.num_of_hyperplanes() << ", n= " << P.dimension() << std::endl; + stream << "Number of non Zeros: " << crhmc_problem.nnz() << std::endl; + stream << "min ess " << min_ess << std::endl; + stream << "Average time per sample: " << ETA / max_actual_draws << "us" + << std::endl; + stream << "Average time per independent sample: " << ETA / min_ess << "us" + << std::endl; + stream << "Step size (final): " << crhmc.solver->eta << std::endl; + stream << "Discard Ratio: " << crhmc.discard_ratio << std::endl; + stream << "Average Acceptance Probability: " + << crhmc.average_acceptance_prob << std::endl; max_psrf = check_interval_psrf(samples); - std::cout << "max_psrf: " << max_psrf << std::endl; - std::cout << std::endl; + stream << "max_psrf: " << max_psrf << std::endl; + stream << std::endl; crhmc_stats.method = "CRHMC"; crhmc_stats.walk_length = walk_length; @@ -288,31 +299,33 @@ std::vector> benchmark_polytope_sampling( return std::vector>{rdhr_stats, crhmc_stats}; } -template -void test_benchmark_polytope( - HPolytope &P, std::string &name, bool centered, - double target_time = std::numeric_limits::max(), int walk_length = 1) -{ +template +void test_benchmark_polytope(StreamType &stream, + HPolytope &P, std::string &name, bool centered, + double target_time = std::numeric_limits::max(), int walk_length = 1) { + stream << "CRHMC polytope preparation for " << name << std::endl; std::cout << "CRHMC polytope preparation for " << name << std::endl; std::vector> results; NT step_size = 0; std::pair inner_ball; std::ofstream outfile; - std::cout << name << std::endl; outfile.open("results_" + name + "_new.txt"); P.normalize(); inner_ball = P.ComputeInnerBall(); step_size = inner_ball.second / 10; - results = benchmark_polytope_sampling(P, step_size, walk_length, target_time, - false, centered); + results = benchmark_polytope_sampling(stream, P, step_size, walk_length, target_time, + false, centered); outfile << results[0]; outfile << results[1]; outfile.close(); } -template +template void call_test_benchmark_polytope() { + std::ofstream stream; + stream.open("CRHMC_SIMD_" + std::to_string(simdLen) + ".txt"); + stream << "---------------Using simdLen= " << simdLen << "---------------" << std::endl; using Kernel = Cartesian; using Point = typename Kernel::Point; using Hpolytope = HPolytope; @@ -321,7 +334,7 @@ void call_test_benchmark_polytope() { std::string name = "100_skinny_cube"; bool centered = false; double target_time = 20; // secs - test_benchmark_polytope(P, name, false, target_time); + test_benchmark_polytope(stream, P, name, target_time, false); } { @@ -329,23 +342,23 @@ void call_test_benchmark_polytope() { std::string name = "5_cross"; bool centered = false; double target_time = 10; // secs - test_benchmark_polytope(P, name, centered, target_time); + test_benchmark_polytope(stream, P, name, target_time, centered); } { Hpolytope P = generate_simplex(100, false); std::string name = "100_simplex"; bool centered = false; - double target_time = 15; // secs - test_benchmark_polytope(P, name, centered, target_time); + double target_time = 20; // secs + test_benchmark_polytope(stream, P, name, target_time, centered); } { Hpolytope P = generate_prod_simplex(50, false); std::string name = "50_prod_simplex"; bool centered = false; - double target_time = 15; // secs - test_benchmark_polytope(P, name, centered, target_time); + double target_time = 20; // secs + test_benchmark_polytope(stream, P, name, target_time, centered); } { @@ -353,35 +366,36 @@ void call_test_benchmark_polytope() { std::string name = "10_birkhoff"; bool centered = false; double target_time = 15; // secs - test_benchmark_polytope(P, name, centered, target_time); + test_benchmark_polytope(stream, P, name, target_time, centered); } - if (exists_check("netlib/afiro.ine")) - { - Hpolytope P = read_polytope("netlib/afiro.ine"); + if (exists_check("../test/netlib/afiro.ine")) { + Hpolytope P = read_polytope("../test/netlib/afiro.ine"); std::string name = "afiro"; bool centered = true; double target_time = 100; // secs - test_benchmark_polytope(P, name, centered, target_time); + test_benchmark_polytope(stream, P, name, target_time, centered); } - if (exists_check("metabolic_full_dim/polytope_e_coli.ine")) - { + if (exists_check("../test/metabolic_full_dim/polytope_e_coli.ine")) { Hpolytope P = - read_polytope("metabolic_full_dim/polytope_e_coli.ine"); + read_polytope("../test/metabolic_full_dim/polytope_e_coli.ine"); std::string name = "e_coli"; bool centered = true; double target_time = 600; // secs - test_benchmark_polytope(P, name, centered, target_time); + test_benchmark_polytope(stream, P, name, target_time, centered); } + + stream.close(); } int main() { - std::cout << "---------------CRHMC polytope sampling benchmarking---------------" << std::endl << std::endl; - call_test_benchmark_polytope(); + call_test_benchmark_polytope(); + call_test_benchmark_polytope(); + call_test_benchmark_polytope(); return 0; } diff --git a/test/crhmc_polytope_preparation_test.cpp b/test/crhmc_polytope_preparation_test.cpp index 6da13269b..db6fa0674 100644 --- a/test/crhmc_polytope_preparation_test.cpp +++ b/test/crhmc_polytope_preparation_test.cpp @@ -117,6 +117,8 @@ template void test_crhmc_dependent_polytope() { using VT = Eigen::Matrix; using Input = crhmc_input; using CrhmcProblem = crhmc_problem; + using SpMat = typename CrhmcProblem::SpMat; + using InputSparse = crhmc_input; using PolytopeType = HPolytope; using Opts = opts; unsigned d = 3; @@ -131,6 +133,13 @@ template void test_crhmc_dependent_polytope() { options.EnableReordering = true; CrhmcProblem P = CrhmcProblem(input, options); CHECK(P.equations() == 2); + + SpMat A = Aeq.sparseView(); + InputSparse input_sparse = InputSparse(d); + input_sparse.Aeq = A; + input_sparse.beq = beq; + crhmc_problem Q = crhmc_problem(input_sparse,options); + CHECK(Q.equations() == 2); } template void test_center_computation() { @@ -188,6 +197,7 @@ template void call_test_center_computation(){ std::cout << "--- Testing CRHMC polytope-center computation" << std::endl; test_center_computation(); } + TEST_CASE("test_preparation_crhmc") { call_test_crhmc_preprocesssing(); } diff --git a/test/crhmc_sampling_test.cpp b/test/crhmc_sampling_test.cpp index 2bd1b7995..a13a6e299 100644 --- a/test/crhmc_sampling_test.cpp +++ b/test/crhmc_sampling_test.cpp @@ -16,6 +16,8 @@ #include "ode_solvers.hpp" #include "preprocess/crhmc/crhmc_input.h" #include "preprocess/crhmc/crhmc_problem.h" +#include "preprocess/crhmc/crhmc_problem.h" +#include "preprocess/crhmc/crhmc_utils.h" #include "random.hpp" #include "random/normal_distribution.hpp" #include "random/uniform_int.hpp" @@ -33,6 +35,7 @@ #include #include #include "preprocess/svd_rounding.hpp" +#include "sampling/sampling.hpp" struct InnerBallFunctor { // Gaussian density centered at the inner ball center @@ -178,15 +181,14 @@ template = skip_samples) { - Point x = sampler.getPoint(); + Point x = Point(sampler.getPoints().rowwise().sum()); mean = mean + x; } @@ -228,12 +230,11 @@ Polytope read_polytope(std::string filename) { return P; } -template +template void crhmc_polytope_sampling( Polytope &P, NT eta = NT(-1), unsigned int walk_length = 1, bool rounding = false, bool centered = false, - unsigned int max_draws = 80000, unsigned int num_burns = 20000) -{ + unsigned int max_draws = 80000, unsigned int num_burns = 20000) { using Kernel = Cartesian; using Point = typename Kernel::Point; using RandomNumberGenerator = BoostRandomNumberGenerator; @@ -246,8 +247,7 @@ void crhmc_polytope_sampling( NegativeGradientFunctor, HessianFunctor>; using CrhmcProblem = crhmc_problem; using Opts = opts; - using Solver = ImplicitMidpointODESolver; + using Solver = ImplicitMidpointODESolver; std::pair inner_ball; if (centered) { @@ -286,8 +286,8 @@ void crhmc_polytope_sampling( NT max_psrf; Opts options; - CRHMCWalk::parameters crhmc_params(F, dim, - options); + options.simdLen = simdLen; + CRHMCWalk::parameters crhmc_params(F, dim,options); Input input = Input(P.dimension(), f, F, H); input.Aineq = P.get_mat(); input.bineq = P.get_vec(); @@ -318,11 +318,16 @@ void crhmc_polytope_sampling( std::cout << std::endl; std::cout << "Sampling" << std::endl; - for (unsigned int i = 0; i < max_actual_draws; i++) { + for (unsigned int i = 0; i < std::ceil(max_actual_draws / simdLen); i++) { for (int k = 0; k < walk_length; k++) { crhmc.apply(rng, 1); } - samples.col(i) = crhmc.getPoint().getCoefficients(); + MT sample = crhmc.getPoints(); + if (i * simdLen + simdLen - 1 < max_actual_draws) { + samples(Eigen::all, Eigen::seq(i * simdLen, i * simdLen + simdLen - 1)) = sample; + } else { + samples(Eigen::all, Eigen::seq(i * simdLen, max_actual_draws - 1)) = sample(Eigen::all, Eigen::seq(0, max_actual_draws - 1 - simdLen * i)); + } if (i % 1000 == 0 && i > 0) std::cout << "."; } @@ -340,7 +345,7 @@ inline bool exists_check(const std::string &name) { return f.good(); } -template +template void test_sampling_polytope(HPolytope &P, std::string &name, bool centered, int walk_length = 1) { NT step_size = 0; @@ -349,9 +354,9 @@ void test_sampling_polytope(HPolytope &P, std::string &name, bool centered, P.normalize(); inner_ball = P.ComputeInnerBall(); step_size = inner_ball.second / 10; - crhmc_polytope_sampling(P, step_size, walk_length, false, centered); + crhmc_polytope_sampling(P, step_size, walk_length, false, centered); } -template +template void call_test_sampling_polytope() { using Kernel = Cartesian; using Point = typename Kernel::Point; @@ -362,35 +367,35 @@ void call_test_sampling_polytope() { Hpolytope P = generate_skinny_cube(100, false); std::string name = "100_skinny_cube"; bool centered = false; - test_sampling_polytope(P, name, false); + test_sampling_polytope(P, name, false); } { Hpolytope P = generate_cross(5, false); std::string name = "5_cross"; bool centered = false; - test_sampling_polytope(P, name, centered); + test_sampling_polytope(P, name, centered); } { Hpolytope P = generate_simplex(100, false); std::string name = "100_simplex"; bool centered = false; - test_sampling_polytope(P, name, centered); + test_sampling_polytope(P, name, centered); } { Hpolytope P = generate_prod_simplex(50, false); std::string name = "50_prod_simplex"; bool centered = false; - test_sampling_polytope(P, name, centered); + test_sampling_polytope(P, name, centered); } { Hpolytope P = generate_birkhoff(10); std::string name = "10_birkhoff"; bool centered = false; - test_sampling_polytope(P, name, centered); + test_sampling_polytope(P, name, centered); } } @@ -443,7 +448,7 @@ void benchmark_cube_crhmc() { } } -template +template void test_crhmc() { using Kernel = Cartesian; using Point = typename Kernel::Point; @@ -458,8 +463,7 @@ void test_crhmc() { crhmc_input; using CrhmcProblem = crhmc_problem; using RandomNumberGenerator = BoostRandomNumberGenerator; - using Solver = ImplicitMidpointODESolver; + using Solver = ImplicitMidpointODESolver; using Opts = opts; IsotropicQuadraticFunctor::parameters params; params.order = 2; @@ -468,12 +472,16 @@ void test_crhmc() { RandomNumberGenerator rng(1); unsigned int dim = 10; Opts options; + options.simdLen = simdLen; + options.DynamicWeight=false; + options.DynamicStepSize=false; + options.DynamicRegularizer=false; CRHMCWalk::parameters crhmc_params(g, dim, options); Input input = Input(dim, f, g); input.lb = -VT::Ones(dim); input.ub = VT::Ones(dim); - CrhmcProblem P = CrhmcProblem(input); + CrhmcProblem P = CrhmcProblem(input,options); Point x0(dim); CRHMCWalk::Walk @@ -486,13 +494,68 @@ template void call_test_crhmc() { std::cout << "--- Testing Constrained Riemannian Hamiltonian Monte Carlo" << std::endl; - test_crhmc(); + std::cout << "------------SIMDLEN=1-------------------\n" + << std::endl; + test_crhmc(); + std::cout << "------------SIMDLEN=4-------------------\n" + << std::endl; + test_crhmc(); } template void call_test_benchmark_cube_crhmc() { benchmark_cube_crhmc(); } +template +void test_polytope_sampling_sparse_problem(ConstraintProblem &problem, int n_samples = 80000, int n_burns = 20000){ + using NT = typename Point::FT; + using VT = Eigen::Matrix; + using MT = Eigen::Matrix; + using Func = GaussianFunctor::FunctionFunctor; + using Grad = GaussianFunctor::GradientFunctor; + using Hess = GaussianFunctor::HessianFunctor; + using func_params = GaussianFunctor::parameters; + using RNG = BoostRandomNumberGenerator; + func_params params = func_params(Point(problem.dimension()), 0.5, 1); + Func* f= new Func(params); + Grad* g= new Grad(params); + Hess* h= new Hess(params); + RNG rng(1); + std::list PointList; + execute_crhmc, Grad, Func, Hess, CRHMCWalk, simdLen>(problem, rng, PointList, 1, n_samples, n_burns, g, f, h, true); + std::cout<<"Here--------------------------\n"; + MT samples = MT(PointList.front().dimension(), PointList.size()); + int i=0; + for (typename std::list::iterator it = PointList.begin(); it != PointList.end(); ++it){ + samples.col(i) = (*it).getCoefficients(); + i++; + } + NT max_psrf = check_interval_psrf(samples); + std::cout<<"PSRF: "< +void call_test_polytope_sampling_sparse_problem(){ + std::cout << " ---Sampling sparse problems " << std::endl; + using SpMat = Eigen::SparseMatrix; + using Kernel = Cartesian; + using Point = typename Kernel::Point; + using VT = Eigen::Matrix; + using ConstraintProblem =constraint_problem; + if(exists_check("../test/netlib/degen2.mm")){ + std::cout<<"Problem name: degen2" << std::endl; + SpMat A; + VT b, lb, ub; + int dimension; + load_problem(A, b, lb, ub, dimension, "../test/netlib/degen2"); + ConstraintProblem problem = ConstraintProblem(dimension); + problem.set_equality_constraints(A, b); + problem.set_bounds(lb, ub); + test_polytope_sampling_sparse_problem(problem); + } +} TEST_CASE("crhmc") { call_test_crhmc(); } @@ -502,5 +565,19 @@ TEST_CASE("benchmark_crhmc_cube") { } TEST_CASE("test_polytope_sampling_crhmc") { - call_test_sampling_polytope(); + std::cout << "------------SIMDLEN=1-------------------\n" + << std::endl; + call_test_sampling_polytope(); + std::cout << "------------SIMDLEN=4-------------------\n" + << std::endl; + call_test_sampling_polytope(); +} + +TEST_CASE("test_sampling_sparse_problem") { + std::cout << "------------SIMDLEN=1-------------------\n" + << std::endl; + call_test_polytope_sampling_sparse_problem(); + std::cout << "------------SIMDLEN=4-------------------\n" + << std::endl; + call_test_polytope_sampling_sparse_problem(); } diff --git a/test/netlib/degen2.mm b/test/netlib/degen2.mm new file mode 100644 index 000000000..0232eff96 --- /dev/null +++ b/test/netlib/degen2.mm @@ -0,0 +1,4447 @@ +%%MatrixMarket matrix coordinate real general +% Generated 28-Oct-2022 +444 758 4444 +1 1 1 +2 2 1 +3 3 1 +225 4 1 +226 5 1 +227 6 1 +228 7 1 +229 8 1 +230 9 1 +231 10 1 +232 11 1 +233 12 1 +234 13 1 +235 14 1 +236 15 1 +237 16 1 +238 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check_norm_progress(Solver &solver, int num_steps, std::vector target, template void test_implicit_midpoint() { typedef Cartesian Kernel; typedef typename Kernel::Point Point; - typedef std::vector pts; + using MT = Eigen::Matrix; + using VT = Eigen::Matrix; + typedef std::vector pts; typedef GaussianFunctor::GradientFunctor grad; typedef GaussianFunctor::FunctionFunctor func; typedef GaussianFunctor::parameters func_params; @@ -172,13 +174,14 @@ template void test_implicit_midpoint() { Input input = Input(d, f, F); input.lb = -VT::Ones(d); input.ub = VT::Ones(d); - CrhmcProblem P = CrhmcProblem(input); + opts.EnableReordering=false; + opts.DynamicWeight = false; + CrhmcProblem P = CrhmcProblem(input,opts); d = P.dimension(); - Point x0 = Point(d); - Point v0 = Point::all_ones(d); + MT x0 = MT::Zero(d,1); + MT v0 = MT::Ones(d,1); pts q{x0, v0}; opts.solver_accuracy_threshold = 1e-2; - opts.DynamicWeight = false; ImplicitMidpointODESolver implicit_midpoint_solver = ImplicitMidpointODESolver(0, 0.01, q, From e767f4669c19ff019eb47aa2ecd1dba14cb424b6 Mon Sep 17 00:00:00 2001 From: Vissarion Fisikopoulos Date: Wed, 15 Nov 2023 11:57:30 +0200 Subject: [PATCH 04/35] Update CODE_OF_CONDUCT.md --- CODE_OF_CONDUCT.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 09827eb16..272634eb9 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -55,7 +55,7 @@ further defined and clarified by project maintainers. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported by contacting the project team at . All +reported by contacting the project team at geomscale@gmail.com. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. From c9fb177a163f45fa9d5bdce0d9abf0dba73740d1 Mon Sep 17 00:00:00 2001 From: Vissarion Fisikopoulos Date: Fri, 9 Feb 2024 15:35:47 +0200 Subject: [PATCH 05/35] Add define lp_solve guards to orderpolytope --- include/convex_bodies/orderpolytope.h | 27 ++++++++++++++++++++++++++- 1 file changed, 26 insertions(+), 1 deletion(-) diff --git a/include/convex_bodies/orderpolytope.h b/include/convex_bodies/orderpolytope.h index 66854234b..2df5c6619 100644 --- a/include/convex_bodies/orderpolytope.h +++ b/include/convex_bodies/orderpolytope.h @@ -208,7 +208,32 @@ class OrderPolytope { std::pair ComputeInnerBall() { normalize(); - return ComputeChebychevBall(_A, b); + std::pair inner_ball; + #ifndef DISABLE_LPSOLVE + inner_ball = ComputeChebychevBall(_A, b); // use lpsolve library + #else + + if (inner_ball.second <= NT(0)) { + + NT const tol = 0.00000001; + std::tuple inner_ball = max_inscribed_ball(_A, b, 150, tol); + + // check if the solution is feasible + if (is_in(Point(std::get<0>(inner_ball))) == 0 || std::get<1>(inner_ball) < NT(0) || + std::isnan(std::get<1>(inner_ball)) || std::isinf(std::get<1>(inner_ball)) || + !std::get<2>(inner_ball) || is_inner_point_nan_inf(std::get<0>(inner_ball))) + { + inner_ball.second = -1.0; + } else + { + inner_ball.first = Point(std::get<0>(inner_ball)); + inner_ball.second = std::get<1>(inner_ball); + } + } + #endif + + return inner_ball; + } From f91d4a98c7e75bb4c9c6e39bce6a03d0f434df4e Mon Sep 17 00:00:00 2001 From: Vissarion Fisikopoulos Date: Fri, 9 Feb 2024 16:27:30 +0200 Subject: [PATCH 06/35] Rename LinearProgramFunctor to ExponentialFunctor and add inverse variance parameter --- include/ode_solvers/oracle_functors.hpp | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/include/ode_solvers/oracle_functors.hpp b/include/ode_solvers/oracle_functors.hpp index 3aea450b2..35fc8887b 100644 --- a/include/ode_solvers/oracle_functors.hpp +++ b/include/ode_solvers/oracle_functors.hpp @@ -243,7 +243,7 @@ struct IsotropicLinearFunctor { }; -struct LinearProgramFunctor { +struct ExponentialFunctor { // Sample from linear program c^T x (exponential density) template < @@ -256,8 +256,10 @@ struct LinearProgramFunctor { NT m; // Strong convexity constant NT kappa; // Condition number Point c; // Coefficients of LP objective + NT a; // Inverse variance - parameters(Point c_) : order(2), L(1), m(1), kappa(1), c(c_) {}; + parameters(Point c_) : order(2), L(1), m(1), kappa(1), c(c_), a(1.0) {}; + parameters(Point c_, NT a_) : order(2), L(1), m(1), kappa(1), c(c_), a(a_) {}; }; @@ -277,7 +279,7 @@ struct LinearProgramFunctor { Point operator() (unsigned int const& i, pts const& xs, NT const& t) const { if (i == params.order - 1) { Point y(params.c); - return (-1.0) * y; + return (-params.a) * y; } else { return xs[i + 1]; // returns derivative } @@ -298,7 +300,7 @@ struct LinearProgramFunctor { // The index i represents the state vector index NT operator() (Point const& x) const { - return x.dot(params.c); + return params.a * x.dot(params.c); } }; From b15c9d858b984ed7bd2c853842d887e5d050696e Mon Sep 17 00:00:00 2001 From: vfisikop Date: Wed, 14 Feb 2024 16:18:33 +0200 Subject: [PATCH 07/35] Rename LinearProgramFunctor to ExponentialFunctor, add inverse variance parameter, add define lp_solve guards to orderpolytope --- include/convex_bodies/orderpolytope.h | 27 ++++++++++++++++++++++++- include/ode_solvers/oracle_functors.hpp | 10 +++++---- test/logconcave_sampling_test.cpp | 14 ++++++------- 3 files changed, 39 insertions(+), 12 deletions(-) diff --git a/include/convex_bodies/orderpolytope.h b/include/convex_bodies/orderpolytope.h index 66854234b..2df5c6619 100644 --- a/include/convex_bodies/orderpolytope.h +++ b/include/convex_bodies/orderpolytope.h @@ -208,7 +208,32 @@ class OrderPolytope { std::pair ComputeInnerBall() { normalize(); - return ComputeChebychevBall(_A, b); + std::pair inner_ball; + #ifndef DISABLE_LPSOLVE + inner_ball = ComputeChebychevBall(_A, b); // use lpsolve library + #else + + if (inner_ball.second <= NT(0)) { + + NT const tol = 0.00000001; + std::tuple inner_ball = max_inscribed_ball(_A, b, 150, tol); + + // check if the solution is feasible + if (is_in(Point(std::get<0>(inner_ball))) == 0 || std::get<1>(inner_ball) < NT(0) || + std::isnan(std::get<1>(inner_ball)) || std::isinf(std::get<1>(inner_ball)) || + !std::get<2>(inner_ball) || is_inner_point_nan_inf(std::get<0>(inner_ball))) + { + inner_ball.second = -1.0; + } else + { + inner_ball.first = Point(std::get<0>(inner_ball)); + inner_ball.second = std::get<1>(inner_ball); + } + } + #endif + + return inner_ball; + } diff --git a/include/ode_solvers/oracle_functors.hpp b/include/ode_solvers/oracle_functors.hpp index 3aea450b2..35fc8887b 100644 --- a/include/ode_solvers/oracle_functors.hpp +++ b/include/ode_solvers/oracle_functors.hpp @@ -243,7 +243,7 @@ struct IsotropicLinearFunctor { }; -struct LinearProgramFunctor { +struct ExponentialFunctor { // Sample from linear program c^T x (exponential density) template < @@ -256,8 +256,10 @@ struct LinearProgramFunctor { NT m; // Strong convexity constant NT kappa; // Condition number Point c; // Coefficients of LP objective + NT a; // Inverse variance - parameters(Point c_) : order(2), L(1), m(1), kappa(1), c(c_) {}; + parameters(Point c_) : order(2), L(1), m(1), kappa(1), c(c_), a(1.0) {}; + parameters(Point c_, NT a_) : order(2), L(1), m(1), kappa(1), c(c_), a(a_) {}; }; @@ -277,7 +279,7 @@ struct LinearProgramFunctor { Point operator() (unsigned int const& i, pts const& xs, NT const& t) const { if (i == params.order - 1) { Point y(params.c); - return (-1.0) * y; + return (-params.a) * y; } else { return xs[i + 1]; // returns derivative } @@ -298,7 +300,7 @@ struct LinearProgramFunctor { // The index i represents the state vector index NT operator() (Point const& x) const { - return x.dot(params.c); + return params.a * x.dot(params.c); } }; diff --git a/test/logconcave_sampling_test.cpp b/test/logconcave_sampling_test.cpp index 8e53f489b..1f67b2851 100644 --- a/test/logconcave_sampling_test.cpp +++ b/test/logconcave_sampling_test.cpp @@ -334,12 +334,12 @@ void benchmark_nuts_hmc(bool truncated) { bool automatic_burnin = false; std::chrono::time_point start, stop; - for (unsigned int dim = dim_min; dim <= dim_max; dim+=10) + for (unsigned int dim = dim_min; dim <= dim_max; dim+=10) { MT samples(dim, n_samples); Point x0(dim); NutsHamiltonianMonteCarloWalk::parameters hmc_params(F, dim); - if (truncated) + if (truncated) { Hpolytope P = generate_cube(dim, false); @@ -784,8 +784,8 @@ void benchmark_polytope_linear_program_optimization( typedef std::vector pts; typedef boost::mt19937 RNGType; typedef BoostRandomNumberGenerator RandomNumberGenerator; - typedef LinearProgramFunctor::GradientFunctor NegativeGradientFunctor; - typedef LinearProgramFunctor::FunctionFunctor NegativeLogprobFunctor; + typedef ExponentialFunctor::GradientFunctor NegativeGradientFunctor; + typedef ExponentialFunctor::FunctionFunctor NegativeLogprobFunctor; typedef OptimizationFunctor::GradientFunctor NegativeGradientOptimizationFunctor; typedef OptimizationFunctor::FunctionFunctor lp_params(coeffs); + ExponentialFunctor::parameters lp_params(coeffs); NegativeGradientFunctor F_lp(lp_params); NegativeLogprobFunctor f_lp(lp_params); @@ -1116,8 +1116,8 @@ void call_test_exp_sampling() { typedef HPolytope Hpolytope; std::string name; std::vector> polytopes; - - + + if (exists_check("metabolic_full_dim/e_coli_biomass_function.txt") && exists_check("metabolic_full_dim/polytope_e_coli.ine")){ Point biomass_function_e_coli = load_biomass_function("metabolic_full_dim/e_coli_biomass_function.txt"); polytopes.push_back(std::make_tuple(read_polytope("metabolic_full_dim/polytope_e_coli.ine"), biomass_function_e_coli, "e_coli", true)); From 1ae6a45f54260db9532f6d48b9a8fa90577462dc Mon Sep 17 00:00:00 2001 From: vfisikop Date: Wed, 14 Feb 2024 16:19:03 +0200 Subject: [PATCH 08/35] Remove C11 flags from cran --- R-proj/src/Makevars | 2 -- R-proj/src/Makevars.win | 1 - cran_gen/Makevars | 1 - cran_gen/Makevars.win | 1 - 4 files changed, 5 deletions(-) diff --git a/R-proj/src/Makevars b/R-proj/src/Makevars index b642e2fbe..ea3607acf 100644 --- a/R-proj/src/Makevars +++ b/R-proj/src/Makevars @@ -2,8 +2,6 @@ PKG_CPPFLAGS= -I../../external/boost -I../../external/LPsolve_src/run_headers -I PKG_CXXFLAGS= -Wno-deprecated-declarations -lm -ldl -Wno-ignored-attributes -DBOOST_NO_AUTO_PTR -DDISABLE_NLP_ORACLES -CXX_STD = CXX11 - PKG_LIBS=-LRproj_externals/lp_solve -llp_solve -L../../external/PackedCSparse/qd -lqd $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) $(SHLIB): Rproj_externals/lp_solve/liblp_solve.a ../../external/PackedCSparse/qd/libqd.a diff --git a/R-proj/src/Makevars.win b/R-proj/src/Makevars.win index 17d28e206..83ed4e0c6 100644 --- a/R-proj/src/Makevars.win +++ b/R-proj/src/Makevars.win @@ -1,6 +1,5 @@ PKG_CPPFLAGS=-I../../external/boost -I../../external/LPsolve_src/run_headers -I../../external/minimum_ellipsoid -I../../include -I../../include/convex_bodies/spectrahedra PKG_CXXFLAGS= -Wno-deprecated-declarations -lm -ldl -Wno-ignored-attributes -DBOOST_NO_AUTO_PTR -DDISABLE_NLP_ORACLES -CXX_STD = CXX11 PKG_LIBS=-LRproj_externals/lp_solve -llp_solve -L../../external/PackedCSparse/qd -lqd $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) diff --git a/cran_gen/Makevars b/cran_gen/Makevars index 28c42dcad..8194d4c8a 100644 --- a/cran_gen/Makevars +++ b/cran_gen/Makevars @@ -1,6 +1,5 @@ PKG_CPPFLAGS=-Iexternal -Iexternal/lpsolve/headers/run_headers -Iexternal/minimum_ellipsoid -Iinclude -Iinclude/convex_bodies/spectrahedra PKG_CXXFLAGS= -DBOOST_NO_AUTO_PTR -DDISABLE_NLP_ORACLES -CXX_STD = CXX11 PKG_LIBS=-Lexternal/lpsolve/build/lp_solve -llp_solve -Lexternal/PackedCSparse/qd -lqd $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) diff --git a/cran_gen/Makevars.win b/cran_gen/Makevars.win index b93299131..e4f79d991 100644 --- a/cran_gen/Makevars.win +++ b/cran_gen/Makevars.win @@ -1,6 +1,5 @@ PKG_CPPFLAGS=-Iexternal -Iexternal/lpsolve/headers/run_headers -Iexternal/minimum_ellipsoid -Iinclude -Iinclude/convex_bodies/spectrahedra PKG_CXXFLAGS= -lm -ldl -DBOOST_NO_AUTO_PTR -DDISABLE_NLP_ORACLES -CXX_STD = CXX11 PKG_LIBS=-Lexternal/lpsolve/build/lp_solve -llp_solve -Lexternal/PackedCSparse/qd -lqd $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) From 432b9679e8359ca706c7b8517ca564b047c4272d Mon Sep 17 00:00:00 2001 From: vfisikop Date: Thu, 15 Feb 2024 16:22:25 +0200 Subject: [PATCH 09/35] Build only with lp-solve source code, no need for liblpsolve55.so files --- examples/hpolytope-volume/CMakeLists.txt | 85 ++++++++++-------------- external/cmake-files/LPSolve.cmake | 43 +++++++++++- 2 files changed, 76 insertions(+), 52 deletions(-) diff --git a/examples/hpolytope-volume/CMakeLists.txt b/examples/hpolytope-volume/CMakeLists.txt index 1d923f238..eff88889a 100644 --- a/examples/hpolytope-volume/CMakeLists.txt +++ b/examples/hpolytope-volume/CMakeLists.txt @@ -6,7 +6,6 @@ project( VolEsti ) - CMAKE_MINIMUM_REQUIRED(VERSION 3.11) set(CMAKE_ALLOW_LOOSE_LOOP_CONSTRUCTS true) @@ -15,11 +14,10 @@ if(COMMAND cmake_policy) cmake_policy(SET CMP0003 NEW) endif(COMMAND cmake_policy) - -option(DISABLE_NLP_ORACLES "Disable non-linear oracles (used in collocation)" ON) -option(BUILTIN_EIGEN "Use eigen from ../../external" OFF) +set(CMAKE_EXPORT_COMPILE_COMMANDS "ON") +option(DISABLE_NLP_ORACLES "Disable non-linear oracles (used in collocation)" ON) if(DISABLE_NLP_ORACLES) add_definitions(-DDISABLE_NLP_ORACLES) else() @@ -53,60 +51,47 @@ else() message(STATUS "Library fftw3 found:" ${FFTW3}) endif(NOT IFOPT) - endif(DISABLE_NLP_ORACLES) +# eigen +option(BUILTIN_EIGEN "Use eigen from ../../external" OFF) include("../../external/cmake-files/Eigen.cmake") GetEigen() +if (${CMAKE_VERSION} VERSION_LESS "3.12.0") + add_compile_options(-D "EIGEN_NO_DEBUG") +else () + add_compile_definitions("EIGEN_NO_DEBUG") +endif () + +# boost include("../../external/cmake-files/Boost.cmake") GetBoost() +# lp_solve include("../../external/cmake-files/LPSolve.cmake") GetLPSolve() -# Find lpsolve library -find_library(LP_SOLVE NAMES liblpsolve55.so PATHS /usr/lib/lp_solve) - -if (NOT LP_SOLVE) - message(FATAL_ERROR "This program requires the lp_solve library, and will not be compiled.") -else () - message(STATUS "Library lp_solve found: ${LP_SOLVE}") - - set(CMAKE_EXPORT_COMPILE_COMMANDS "ON") - - include_directories (BEFORE ../../external) - include_directories (BEFORE ../../external/minimum_ellipsoid) - include_directories (BEFORE ../../include/generators) - include_directories (BEFORE ../../include/volume) - include_directories (BEFORE ../../include) - include_directories (BEFORE ../../include/convex_bodies) - include_directories (BEFORE ../../include/random_walks) - include_directories (BEFORE ../../include/annealing) - include_directories (BEFORE ../../include/ode_solvers) - include_directories (BEFORE ../../include/root_finders) - include_directories (BEFORE ../../include/samplers) - include_directories (BEFORE ../../include/lp_oracles) - include_directories (BEFORE ../../include/nlp_oracles) - include_directories (BEFORE ../../include/misc) - include_directories (BEFORE ../../include/optimization) - - # for Eigen - if (${CMAKE_VERSION} VERSION_LESS "3.12.0") - add_compile_options(-D "EIGEN_NO_DEBUG") - else () - add_compile_definitions("EIGEN_NO_DEBUG") - endif () - - - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard - add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler - #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") - add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") - add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-ldl") - add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-DBOOST_NO_AUTO_PTR") - - add_executable (hpolytopeVolume hpolytopeVolume.cpp) - TARGET_LINK_LIBRARIES(hpolytopeVolume ${LP_SOLVE}) - -endif() +include_directories (BEFORE ../../external) +include_directories (BEFORE ../../external/minimum_ellipsoid) +include_directories (BEFORE ../../include/generators) +include_directories (BEFORE ../../include/volume) +include_directories (BEFORE ../../include) +include_directories (BEFORE ../../include/convex_bodies) +include_directories (BEFORE ../../include/random_walks) +include_directories (BEFORE ../../include/annealing) +include_directories (BEFORE ../../include/ode_solvers) +include_directories (BEFORE ../../include/root_finders) +include_directories (BEFORE ../../include/samplers) +include_directories (BEFORE ../../include/lp_oracles) +include_directories (BEFORE ../../include/nlp_oracles) +include_directories (BEFORE ../../include/misc) +include_directories (BEFORE ../../include/optimization) + +add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler +add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") +add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-ldl") +add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-DBOOST_NO_AUTO_PTR") + +add_executable(hpolytopeVolume hpolytopeVolume.cpp) +target_link_libraries(hpolytopeVolume PUBLIC lp_solve) diff --git a/external/cmake-files/LPSolve.cmake b/external/cmake-files/LPSolve.cmake index c5a173837..56bf2ebc2 100644 --- a/external/cmake-files/LPSolve.cmake +++ b/external/cmake-files/LPSolve.cmake @@ -26,7 +26,46 @@ function(GetLPSolve) message(STATUS "lp_solve library found: ${LP_SOLVE_DIR}") endif() - - include_directories(${LP_SOLVE_DIR}) + + #to disable interactive mode of lp_solve lex parser + add_compile_options(-DYY_NEVER_INTERACTIVE) + + add_compile_options(-DLoadInverseLib=0) + add_compile_options(-DLoadLanguageLib=0) + add_compile_definitions(RoleIsExternalInvEngine) + add_compile_definitions(INVERSE_ACTIVE=3) + + include_directories (BEFORE ${LP_SOLVE_DIR}) + include_directories (BEFORE ${LP_SOLVE_DIR}/bfp) + include_directories (BEFORE ${LP_SOLVE_DIR}/bfp/bfp_LUSOL) + include_directories (BEFORE ${LP_SOLVE_DIR}/bfp/bfp_LUSOL/LUSOL) + include_directories (BEFORE ${LP_SOLVE_DIR}/colamd) + include_directories (BEFORE ${LP_SOLVE_DIR}/shared) + + add_library (lp_solve + ${LP_SOLVE_DIR}/bfp/bfp_LUSOL/lp_LUSOL.c + ${LP_SOLVE_DIR}/bfp/bfp_LUSOL/LUSOL/lusol.c + ${LP_SOLVE_DIR}/colamd/colamd.c + ${LP_SOLVE_DIR}/ini.c + ${LP_SOLVE_DIR}/shared/commonlib.c + ${LP_SOLVE_DIR}/shared/mmio.c + ${LP_SOLVE_DIR}/shared/myblas.c + ${LP_SOLVE_DIR}/lp_crash.c + ${LP_SOLVE_DIR}/lp_Hash.c + ${LP_SOLVE_DIR}/lp_lib.c + ${LP_SOLVE_DIR}/lp_matrix.c + ${LP_SOLVE_DIR}/lp_MDO.c + ${LP_SOLVE_DIR}/lp_mipbb.c + ${LP_SOLVE_DIR}/lp_MPS.c + ${LP_SOLVE_DIR}/lp_params.c + ${LP_SOLVE_DIR}/lp_presolve.c + ${LP_SOLVE_DIR}/lp_price.c + ${LP_SOLVE_DIR}/lp_pricePSE.c + ${LP_SOLVE_DIR}/lp_report.c + ${LP_SOLVE_DIR}/lp_scale.c + ${LP_SOLVE_DIR}/lp_simplex.c + ${LP_SOLVE_DIR}/lp_SOS.c + ${LP_SOLVE_DIR}/lp_utils.c + ${LP_SOLVE_DIR}/lp_wlp.c) endfunction() From 7da53c4e2b1657790f04439c4c42e7d4969d07ff Mon Sep 17 00:00:00 2001 From: vfisikop Date: Thu, 15 Feb 2024 17:10:32 +0200 Subject: [PATCH 10/35] Update tests and some examples to buid with lp solve from sources --- .github/workflows/cmake-clang.yml | 2 +- .github/workflows/cmake-gcc.yml | 2 +- examples/crhmc_prepare/CMakeLists.txt | 76 ++-- examples/vpolytope-volume/CMakeLists.txt | 75 ++-- test/CMakeLists.txt | 518 +++++++++++------------ 5 files changed, 321 insertions(+), 352 deletions(-) diff --git a/.github/workflows/cmake-clang.yml b/.github/workflows/cmake-clang.yml index caa22fff2..520b33159 100644 --- a/.github/workflows/cmake-clang.yml +++ b/.github/workflows/cmake-clang.yml @@ -30,7 +30,7 @@ jobs: steps: - uses: actions/checkout@v1 - run: sudo apt-get update || true; - sudo apt-get install ${{ matrix.config.compiler_pkg }} lp-solve; + sudo apt-get install ${{ matrix.config.compiler_pkg }} lp-solve libopenblas-dev; rm -rf build; mkdir build; cd build; diff --git a/.github/workflows/cmake-gcc.yml b/.github/workflows/cmake-gcc.yml index e42805492..f18e358ac 100644 --- a/.github/workflows/cmake-gcc.yml +++ b/.github/workflows/cmake-gcc.yml @@ -29,7 +29,7 @@ jobs: steps: - uses: actions/checkout@v1 - run: sudo apt-get update || true; - sudo apt-get install ${{ matrix.config.compiler }} lp-solve; + sudo apt-get install ${{ matrix.config.compiler }} lp-solve libopenblas-dev; rm -rf build; mkdir build; cd build; diff --git a/examples/crhmc_prepare/CMakeLists.txt b/examples/crhmc_prepare/CMakeLists.txt index 35c31e9e1..3575c4a46 100644 --- a/examples/crhmc_prepare/CMakeLists.txt +++ b/examples/crhmc_prepare/CMakeLists.txt @@ -76,52 +76,42 @@ GetLPSolve() include("../../external/cmake-files/QD.cmake") GetQD() -# Find lpsolve library -find_library(LP_SOLVE NAMES liblpsolve55.so PATHS /usr/lib/lp_solve) - -if (NOT LP_SOLVE) - message(FATAL_ERROR "This program requires the lp_solve library, and will not be compiled.") -else () - - message(STATUS "Library lp_solve found: ${LP_SOLVE}") - set(CMAKE_EXPORT_COMPILE_COMMANDS "ON") - - if (USE_MKL) - find_library(BLAS NAMES libblas.so libblas.dylib PATHS /usr/local/Cellar/lapack/3.9.1_1/lib /usr/lib/x86_64-linux-gnu /usr/lib/i386-linux-gnu /usr/local/Cellar/openblas/0.3.15_1/lib /usr/lib) - find_library(GFORTRAN NAME libgfortran.dylib PATHS /usr/local/Cellar/gcc/10.2.0_4/lib/gcc/10) - find_library(LAPACK NAME liblapack.dylib PATHS /usr/lib) - find_library(OPENMP NAME libiomp5.dylib PATHS /opt/intel/oneapi/compiler/2021.1.1/mac/compiler/lib) - - include_directories (BEFORE ${MKLROOT}/include) - set(PROJECT_LIBS ${BLAS_LIBRARIES} ${LAPACK_LIBRARIES} ${GFORTRAN_LIBRARIES}) - set(MKL_LINK "-L${MKLROOT}/lib -Wl,-rpath,${MKLROOT}/lib -lmkl_intel_ilp64 -lmkl_sequential -lmkl_core -lpthread -lm -ldl") - add_definitions(-DEIGEN_USE_MKL_ALL) - else() - set(MKL_LINK "") - endif(USE_MKL) +set(CMAKE_EXPORT_COMPILE_COMMANDS "ON") + +if (USE_MKL) + find_library(BLAS NAMES libblas.so libblas.dylib PATHS /usr/local/Cellar/lapack/3.9.1_1/lib /usr/lib/x86_64-linux-gnu /usr/lib/i386-linux-gnu /usr/local/Cellar/openblas/0.3.15_1/lib /usr/lib) + find_library(GFORTRAN NAME libgfortran.dylib PATHS /usr/local/Cellar/gcc/10.2.0_4/lib/gcc/10) + find_library(LAPACK NAME liblapack.dylib PATHS /usr/lib) + find_library(OPENMP NAME libiomp5.dylib PATHS /opt/intel/oneapi/compiler/2021.1.1/mac/compiler/lib) + + include_directories (BEFORE ${MKLROOT}/include) + set(PROJECT_LIBS ${BLAS_LIBRARIES} ${LAPACK_LIBRARIES} ${GFORTRAN_LIBRARIES}) + set(MKL_LINK "-L${MKLROOT}/lib -Wl,-rpath,${MKLROOT}/lib -lmkl_intel_ilp64 -lmkl_sequential -lmkl_core -lpthread -lm -ldl") + add_definitions(-DEIGEN_USE_MKL_ALL) +else() + set(MKL_LINK "") +endif(USE_MKL) - include_directories (BEFORE ../../external) - include_directories (BEFORE ../../external/minimum_ellipsoid) - include_directories (BEFORE ../../include/) +include_directories (BEFORE ../../external) +include_directories (BEFORE ../../external/minimum_ellipsoid) +include_directories (BEFORE ../../include/) - # for Eigen - if (${CMAKE_VERSION} VERSION_LESS "3.12.0") - add_compile_options(-D "EIGEN_NO_DEBUG") - else () - add_compile_definitions("EIGEN_NO_DEBUG") - endif () +# for Eigen +if (${CMAKE_VERSION} VERSION_LESS "3.12.0") + add_compile_options(-D "EIGEN_NO_DEBUG") +else () + add_compile_definitions("EIGEN_NO_DEBUG") +endif () - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard - #add_definitions(${CMAKE_CXX_FLAGS} "-g") # enable debuger - set(ADDITIONAL_FLAGS "-march=native -DSIMD_LEN=0 -DTIME_KEEPING") - add_definitions(${CMAKE_CXX_FLAGS} "-O3 " ${ADDITIONAL_FLAGS}) # optimization of the compiler - #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") - add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") - add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-ldl") - add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-DBOOST_NO_AUTO_PTR") +#add_definitions(${CMAKE_CXX_FLAGS} "-g") # enable debuger +set(ADDITIONAL_FLAGS "-march=native -DSIMD_LEN=0 -DTIME_KEEPING") +add_definitions(${CMAKE_CXX_FLAGS} "-O3 " ${ADDITIONAL_FLAGS}) # optimization of the compiler +#add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") +add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") +add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-ldl") +add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-DBOOST_NO_AUTO_PTR") - add_executable (crhmc_prepare crhmc_prepare.cpp ) - TARGET_LINK_LIBRARIES(crhmc_prepare QD_LIB ${MKL_LINK} ${LP_SOLVE}) +add_executable (crhmc_prepare crhmc_prepare.cpp ) +TARGET_LINK_LIBRARIES(crhmc_prepare QD_LIB ${MKL_LINK}) -endif() diff --git a/examples/vpolytope-volume/CMakeLists.txt b/examples/vpolytope-volume/CMakeLists.txt index 5ffd4784b..e1a0c2576 100644 --- a/examples/vpolytope-volume/CMakeLists.txt +++ b/examples/vpolytope-volume/CMakeLists.txt @@ -65,48 +65,37 @@ GetBoost() include("../../external/cmake-files/LPSolve.cmake") GetLPSolve() -# Find lpsolve library -find_library(LP_SOLVE NAMES liblpsolve55.so PATHS /usr/lib/lp_solve) -if (NOT LP_SOLVE) - message(FATAL_ERROR "This program requires the lp_solve library, and will not be compiled.") +set(CMAKE_EXPORT_COMPILE_COMMANDS "ON") + +include_directories (BEFORE ../../external) +include_directories (BEFORE ../../external/minimum_ellipsoid) +include_directories (BEFORE ../../include/generators) +include_directories (BEFORE ../../include/volume) +include_directories (BEFORE ../../include) +include_directories (BEFORE ../../include/convex_bodies) +include_directories (BEFORE ../../include/random_walks) +include_directories (BEFORE ../../include/annealing) +include_directories (BEFORE ../../include/ode_solvers) +include_directories (BEFORE ../../include/root_finders) +include_directories (BEFORE ../../include/samplers) +include_directories (BEFORE ../../include/lp_oracles) +include_directories (BEFORE ../../include/nlp_oracles) +include_directories (BEFORE ../../include/misc) +include_directories (BEFORE ../../include/optimization) + +# for Eigen +if (${CMAKE_VERSION} VERSION_LESS "3.12.0") + add_compile_options(-D "EIGEN_NO_DEBUG") else () - message(STATUS "Library lp_solve found: ${LP_SOLVE}") - - set(CMAKE_EXPORT_COMPILE_COMMANDS "ON") - - include_directories (BEFORE ../../external) - include_directories (BEFORE ../../external/minimum_ellipsoid) - include_directories (BEFORE ../../include/generators) - include_directories (BEFORE ../../include/volume) - include_directories (BEFORE ../../include) - include_directories (BEFORE ../../include/convex_bodies) - include_directories (BEFORE ../../include/random_walks) - include_directories (BEFORE ../../include/annealing) - include_directories (BEFORE ../../include/ode_solvers) - include_directories (BEFORE ../../include/root_finders) - include_directories (BEFORE ../../include/samplers) - include_directories (BEFORE ../../include/lp_oracles) - include_directories (BEFORE ../../include/nlp_oracles) - include_directories (BEFORE ../../include/misc) - include_directories (BEFORE ../../include/optimization) - - # for Eigen - if (${CMAKE_VERSION} VERSION_LESS "3.12.0") - add_compile_options(-D "EIGEN_NO_DEBUG") - else () - add_compile_definitions("EIGEN_NO_DEBUG") - endif () - - - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard - add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler - #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") - add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") - add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-ldl") - add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-DBOOST_NO_AUTO_PTR") - - add_executable (vpolytopevolume vpolytopevolume.cpp) - TARGET_LINK_LIBRARIES(vpolytopevolume ${LP_SOLVE}) - -endif() + add_compile_definitions("EIGEN_NO_DEBUG") +endif () + +add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler +#add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") +add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") +add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-ldl") +add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-DBOOST_NO_AUTO_PTR") + +add_executable (vpolytopevolume vpolytopevolume.cpp) +TARGET_LINK_LIBRARIES(vpolytopevolume lp_solve) diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index 3adbe4681..a009795cc 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -26,7 +26,7 @@ endif() option(DISABLE_NLP_ORACLES "Disable non-linear oracles (used in collocation)" ON) option(BUILTIN_EIGEN "Use eigen from ../external" OFF) -option(USE_MKL "Use MKL library to build eigen" ON) +option(USE_MKL "Use MKL library to build eigen" OFF) if(DISABLE_NLP_ORACLES) add_definitions(-DDISABLE_NLP_ORACLES) @@ -136,271 +136,261 @@ if(CODE_COVERAGE AND CMAKE_CXX_COMPILER_ID MATCHES "GNU|Clang") endif() endif(CODE_COVERAGE AND CMAKE_CXX_COMPILER_ID MATCHES "GNU|Clang") -find_library(LP_SOLVE NAMES liblpsolve55.so liblpsolve55.dylib PATHS /usr/lib/lp_solve /usr/local/lib) -if (NOT LP_SOLVE) +set(CMAKE_EXPORT_COMPILE_COMMANDS "ON") - message(FATAL_ERROR "This program requires the lp_solve library, and will not be compiled.") +if (USE_MKL) + find_library(BLAS NAMES libblas.so libblas.dylib PATHS /usr/local/Cellar/lapack/3.9.1_1/lib /usr/lib/x86_64-linux-gnu /usr/lib/i386-linux-gnu /usr/local/Cellar/openblas/0.3.15_1/lib /usr/lib) + find_library(GFORTRAN NAME libgfortran.dylib PATHS /usr/local/Cellar/gcc/10.2.0_4/lib/gcc/10) + find_library(LAPACK NAME liblapack.dylib PATHS /usr/lib) + find_library(OPENMP NAME libiomp5.dylib PATHS /opt/intel/oneapi/compiler/2021.1.1/mac/compiler/lib) + include_directories (BEFORE ${MKLROOT}/include) + set(PROJECT_LIBS ${BLAS_LIBRARIES} ${LAPACK_LIBRARIES} ${GFORTRAN_LIBRARIES}) + set(MKL_LINK "-L${MKLROOT}/lib -Wl,-rpath,${MKLROOT}/lib -lmkl_intel_ilp64 -lmkl_sequential -lmkl_core -lpthread -lm -ldl") + add_definitions(-DEIGEN_USE_MKL_ALL) +else() + set(MKL_LINK "") +endif(USE_MKL) + +include_directories (BEFORE ../external) +include_directories (BEFORE ../external/minimum_ellipsoid) +include_directories (BEFORE ../include/generators) +include_directories (BEFORE ../include/volume) +include_directories (BEFORE ../include) +include_directories (BEFORE ../include/integration) +include_directories (BEFORE ../include/convex_bodies) +include_directories (BEFORE ../include/convex_bodies/spectrahedra) +include_directories (BEFORE ../include/annealing) +include_directories (BEFORE ../include/ode_solvers) +include_directories (BEFORE ../include/root_finders) +include_directories (BEFORE ../include/samplers) +include_directories (BEFORE ../include/lp_oracles) +include_directories (BEFORE ../include/nlp_oracles) +include_directories (BEFORE ../include/misc) +include_directories (BEFORE ../test) + +#for Eigen +if (${CMAKE_VERSION} VERSION_LESS "3.12.0") + add_compile_options(-D "EIGEN_NO_DEBUG") else () + add_compile_definitions("EIGEN_NO_DEBUG") +endif () + +add_definitions(${CMAKE_CXX_FLAGS} "-g") # enable debuger +#add_definitions(${CMAKE_CXX_FLAGS} "-Wint-in-bool-context") +#add_definitions(${CMAKE_CXX_FLAGS} "-Wall") + +add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler +#add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") +add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") +add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-ldl") +add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-DBOOST_NO_AUTO_PTR") +add_definitions(${CMAKE_CXX_FLAGS} "-DMKL_ILP64") +#add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgslcblas") +#add_definitions( "-O3 -lgsl -lm -ldl -lgslcblas" ) + +add_executable (new_volume_example new_volume_example.cpp) +add_executable (benchmarks_sob benchmarks_sob.cpp) +add_executable (benchmarks_cg benchmarks_cg.cpp) +add_executable (benchmarks_cb benchmarks_cb.cpp) + +add_library(test_main OBJECT test_main.cpp) + +add_executable (mcmc_diagnostics_test mcmc_diagnostics_test.cpp $) +add_test(NAME test_psrf COMMAND mcmc_diagnostics_test -tc=psrf) +add_test(NAME test_univariate_psrf COMMAND mcmc_diagnostics_test -tc=univariate_psrf) +add_test(NAME test_interval_psrf COMMAND mcmc_diagnostics_test -tc=interval_psrf) +add_test(NAME test_ess COMMAND mcmc_diagnostics_test -tc=ess) +add_test(NAME test_geweke COMMAND mcmc_diagnostics_test -tc=geweke) +add_test(NAME test_raftery COMMAND mcmc_diagnostics_test -tc=raftery) + +add_executable (sampling_test sampling_test.cpp $) +add_test(NAME test_dikin COMMAND sampling_test -tc=dikin) +add_test(NAME test_john COMMAND sampling_test -tc=john) +add_test(NAME test_vaidya COMMAND sampling_test -tc=vaidya) +add_test(NAME test_brdhr COMMAND sampling_test -tc=brdhr) +add_test(NAME test_bcdhr COMMAND sampling_test -tc=bcdhr) +add_test(NAME test_grdhr COMMAND sampling_test -tc=grdhr) +add_test(NAME test_gbaw COMMAND sampling_test -tc=gbaw) +add_test(NAME test_ghmc COMMAND sampling_test -tc=ghmc) + +add_executable (mmcs_test mmcs_test.cpp $) +add_test(NAME test_mmcs COMMAND mmcs_test -tc=mmcs) + +add_executable (ode_solvers_test ode_solvers_test.cpp $) +add_test(NAME ode_solvers_test_first_order + COMMAND ode_solvers_test -tc=first_order) +add_test(NAME ode_solvers_test_second_order + COMMAND ode_solvers_test -tc=second_order) + +add_executable (root_finders_test root_finders_test.cpp $) +add_test(NAME root_finders_test_root_finders + COMMAND root_finders_test -tc=root_finders) + +#add_executable (benchmarks_crhmc benchmarks_crhmc.cpp ) +#add_executable (benchmarks_crhmc_sampling benchmarks_crhmc_sampling.cpp ) + +add_executable (crhmc_polytope_preparation_test crhmc_polytope_preparation_test.cpp $) +add_test(NAME crhmc_polytope_test_preparation + COMMAND crhmc_polytope_preparation_test -tc=test_preparation_crhmc) +add_test(NAME crhmc_test_fixed_vars + COMMAND crhmc_polytope_preparation_test -tc=test_fixed_vars_crhmc) +add_test(NAME crhmc_test_dep_vars + COMMAND crhmc_polytope_preparation_test -tc=test_dep_vars_crhmc) +add_test(NAME crhmc_test_center_computation + COMMAND crhmc_polytope_preparation_test -tc=test_center_computation) + +add_executable (boundary_oracles_test boundary_oracles_test.cpp $) +add_test(NAME boundary_oracles_test_h_poly_oracles + COMMAND boundary_oracles_test -tc=h_poly_oracles) + +add_executable (volume_sob_hpolytope volume_sob_hpolytope.cpp $) +add_test(NAME volume_sob_hpolytope_cube COMMAND volume_sob_hpolytope -tc=cube) +add_test(NAME volume_sob_hpolytope_cross COMMAND volume_sob_hpolytope -tc=cross) +add_test(NAME volume_sob_hpolytope_birkhoff COMMAND volume_sob_hpolytope -tc=birk) +add_test(NAME volume_sob_hpolytope_prod_simplex COMMAND volume_sob_hpolytope -tc=prod_simplex) +add_test(NAME volume_sob_hpolytope_simplex COMMAND volume_sob_hpolytope -tc=simplex) +add_test(NAME volume_sob_hpolytope_skinny_cube COMMAND volume_sob_hpolytope -tc=skinny_cube) +add_test(NAME volume_sob_hpolytope_cube_overflow COMMAND volume_sob_hpolytope -tc=cube_overflow) +set_property(TEST volume_sob_hpolytope_cube_overflow PROPERTY TIMEOUT 1) + +add_executable (volume_sob_vpolytope volume_sob_vpolytope.cpp $) +add_test(NAME volume_sob_vpolytope_cube COMMAND volume_sob_vpolytope -tc=cube) +add_test(NAME volume_sob_vpolytope_cross COMMAND volume_sob_vpolytope -tc=cross) +add_test(NAME volume_sob_vpolytope_simplex COMMAND volume_sob_vpolytope -tc=simplex) + +add_executable (volume_cg_hpolytope volume_cg_hpolytope.cpp $) +add_test(NAME volume_cg_hpolytope_cube COMMAND volume_cg_hpolytope -tc=cube) +add_test(NAME volume_cg_hpolytope_cross COMMAND volume_cg_hpolytope -tc=cross) +add_test(NAME volume_cg_hpolytope_birkhoff COMMAND volume_cg_hpolytope -tc=birk) +add_test(NAME volume_cg_hpolytope_prod_simplex COMMAND volume_cg_hpolytope -tc=prod_simplex) +add_test(NAME volume_cg_hpolytope_simplex COMMAND volume_cg_hpolytope -tc=simplex) +add_test(NAME volume_cg_hpolytope_skinny_cube COMMAND volume_cg_hpolytope -tc=skinny_cube) + +add_executable (volume_cg_vpolytope volume_cg_vpolytope.cpp $) +add_test(NAME volume_cg_vpolytope_cube COMMAND volume_cg_vpolytope -tc=cube) +add_test(NAME volume_cg_vpolytope_cross COMMAND volume_cg_vpolytope -tc=cross) +add_test(NAME volume_cg_vpolytope_simplex COMMAND volume_cg_vpolytope -tc=simplex) + +add_executable (volume_cb_hpolytope volume_cb_hpolytope.cpp $) +add_test(NAME volume_cb_hpolytope_cube COMMAND volume_cb_hpolytope -tc=cube) +add_test(NAME volume_cb_hpolytope_cross COMMAND volume_cb_hpolytope -tc=cross) +add_test(NAME volume_cb_hpolytope_birkhoff COMMAND volume_cb_hpolytope -tc=birk) +add_test(NAME volume_cb_hpolytope_prod_simplex COMMAND volume_cb_hpolytope -tc=prod_simplex) +add_test(NAME volume_cb_hpolytope_simplex COMMAND volume_cb_hpolytope -tc=simplex) +add_test(NAME volume_cb_hpolytope_skinny_cube COMMAND volume_cb_hpolytope -tc=skinny_cube) + +add_executable (volume_cb_vpolytope volume_cb_vpolytope.cpp $) +add_test(NAME volume_cb_vpolytope_cube COMMAND volume_cb_vpolytope -tc=cube) +add_test(NAME volume_cb_vpolytope_cross COMMAND volume_cb_vpolytope -tc=cross) +add_test(NAME volume_cb_vpolytope_simplex COMMAND volume_cb_vpolytope -tc=simplex) + +add_executable (volume_cb_zonotopes volume_cb_zonotopes.cpp $) +add_test(NAME volume_cb_zonotopes_uniform_zonotopes + COMMAND volume_cb_zonotopes -tc=uniform_zonotopes) + +add_executable (volume_cb_vpoly_intersection_vpoly volume_cb_vpoly_intersection_vpoly.cpp $) +add_test(NAME volume_cb_vpoly_intersection_vpoly_random_vpoly_sphere + COMMAND volume_cb_vpoly_intersection_vpoly -tc=random_vpoly_sphere) + +add_executable (new_rounding_test new_rounding_test.cpp $) +add_test(NAME test_round_min_ellipsoid + COMMAND new_rounding_test -tc=round_min_ellipsoid) +add_test(NAME test_round_max_ellipsoid + COMMAND new_rounding_test -tc=round_max_ellipsoid) +add_test(NAME test_round_svd + COMMAND new_rounding_test -tc=round_svd) + +add_executable (logconcave_sampling_test logconcave_sampling_test.cpp $) +add_test(NAME logconcave_sampling_test_hmc + COMMAND logconcave_sampling_test -tc=hmc) +add_test(NAME logconcave_sampling_test_uld + COMMAND logconcave_sampling_test -tc=uld) +add_test(NAME logconcave_sampling_test_exponential_biomass_sampling + COMMAND logconcave_sampling_test -tc=exponential_biomass_sampling) +add_test(NAME logconcave_sampling_test_nuts_hmc_truncated + COMMAND logconcave_sampling_test -tc=benchmark_nuts_hmc_truncated) +add_test(NAME logconcave_sampling_test_nuts_hmc + COMMAND logconcave_sampling_test -tc=benchmark_nuts_hmc) + + + +add_executable (crhmc_sampling_test crhmc_sampling_test.cpp $) +add_test(NAME crhmc_sampling_test_crhmc + COMMAND crhmc_sampling_test -tc=crhmc) +add_test(NAME crhmc_test_polytope_sampling + COMMAND crhmc_sampling_test -tc=test_polytope_sampling_crhmc) +add_test(NAME crhmc_test_sparse_sampling + COMMAND crhmc_sampling_test -tc=test_sampling_sparse_problem) +add_executable (simple_mc_integration simple_mc_integration.cpp $) +add_test(NAME simple_mc_integration_over_limits + COMMAND simple_mc_integration -tc=rectangle) +add_test(NAME simple_mc_integration_over_cubes + COMMAND simple_mc_integration -tc=cube) +add_test(NAME simple_mc_integration_over_simplices + COMMAND simple_mc_integration -tc=simplex) +add_test(NAME simple_mc_integration_over_product_simplices + COMMAND simple_mc_integration -tc=prod_simplex) +add_test(NAME simple_mc_integration_over_cross_polytopes + COMMAND simple_mc_integration -tc=cross) +add_test(NAME simple_mc_integration_over_birkhoff_polytopes + COMMAND simple_mc_integration -tc=birkhoff) + +add_executable (order_polytope order_polytope.cpp $) +add_test(NAME order_polytope_basics COMMAND order_polytope -tc=basics) +add_test(NAME order_polytope_line_intersect COMMAND order_polytope -tc=line_intersect) +add_test(NAME order_polytope_reflection COMMAND order_polytope -tc=reflection) +add_test(NAME order_polytope_vec_mult COMMAND order_polytope -tc=vec_mult) + +add_executable (matrix_sampling_test sampling_correlation_matrices_test.cpp $) +add_test(NAME test_corre_spectra_classes COMMAND matrix_sampling_test -tc=corre_spectra) +add_test(NAME test_new_ball_uniform COMMAND matrix_sampling_test -tc=new_ball_uniform) +add_test(NAME test_new_billiard_uniform COMMAND matrix_sampling_test -tc=new_billiard_uniform) +add_test(NAME test_new_accelerated_billiard_uniform COMMAND matrix_sampling_test -tc=new_accelerated_billiard_uniform) +add_test(NAME test_new_ball_uniform_MT COMMAND matrix_sampling_test -tc=new_ball_uniform_MT) +add_test(NAME test_new_rdhr_uniform_MT COMMAND matrix_sampling_test -tc=new_rdhr_uniform_MT) +add_test(NAME test_new_billiard_uniform_MT COMMAND matrix_sampling_test -tc=new_billiard_uniform_MT) +add_test(NAME test_new_accelerated_billiard_uniform_MT COMMAND matrix_sampling_test -tc=new_accelerated_billiard_uniform_MT) + +set(ADDITIONAL_FLAGS "-march=native -DSIMD_LEN=0 -DTIME_KEEPING") + +#set_target_properties(benchmarks_crhmc +# PROPERTIES COMPILE_FLAGS ${ADDITIONAL_FLAGS}) +#set_target_properties(benchmarks_crhmc_sampling +# PROPERTIES COMPILE_FLAGS ${ADDITIONAL_FLAGS}) +set_target_properties(crhmc_polytope_preparation_test + PROPERTIES COMPILE_FLAGS ${ADDITIONAL_FLAGS}) +set_target_properties(crhmc_sampling_test + PROPERTIES COMPILE_FLAGS ${ADDITIONAL_FLAGS}) + +TARGET_LINK_LIBRARIES(new_volume_example lp_solve ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(new_volume_example lp_solve coverage_config) +TARGET_LINK_LIBRARIES(volume_sob_hpolytope lp_solve coverage_config) +TARGET_LINK_LIBRARIES(volume_sob_vpolytope lp_solve coverage_config) +TARGET_LINK_LIBRARIES(volume_cg_hpolytope lp_solve coverage_config) +TARGET_LINK_LIBRARIES(volume_cg_vpolytope lp_solve coverage_config) +TARGET_LINK_LIBRARIES(volume_cb_hpolytope lp_solve coverage_config) +TARGET_LINK_LIBRARIES(volume_cb_vpolytope lp_solve coverage_config) +TARGET_LINK_LIBRARIES(volume_cb_zonotopes lp_solve coverage_config) +TARGET_LINK_LIBRARIES(volume_cb_vpoly_intersection_vpoly lp_solve coverage_config) +TARGET_LINK_LIBRARIES(volume_cb_vpoly_intersection_vpoly lp_solve ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(new_rounding_test lp_solve ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(mcmc_diagnostics_test lp_solve ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(sampling_test lp_solve ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(mmcs_test lp_solve ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(benchmarks_sob lp_solve ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(benchmarks_cg lp_solve ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(benchmarks_cb lp_solve ${MKL_LINK} coverage_config) +#TARGET_LINK_LIBRARIES(benchmarks_crhmc_sampling lp_solve ${MKL_LINK} QD_LIB coverage_config) +#TARGET_LINK_LIBRARIES(benchmarks_crhmc lp_solve ${MKL_LINK} QD_LIB coverage_config) +TARGET_LINK_LIBRARIES(simple_mc_integration lp_solve ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(ode_solvers_test lp_solve ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} QD_LIB coverage_config) +TARGET_LINK_LIBRARIES(boundary_oracles_test lp_solve ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(root_finders_test ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(crhmc_polytope_preparation_test ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} QD_LIB coverage_config) +TARGET_LINK_LIBRARIES(logconcave_sampling_test lp_solve ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(crhmc_sampling_test lp_solve ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} QD_LIB coverage_config) +TARGET_LINK_LIBRARIES(order_polytope lp_solve coverage_config) +TARGET_LINK_LIBRARIES(matrix_sampling_test lp_solve ${MKL_LINK} coverage_config) - message(STATUS "Library lp_solve found: ${LP_SOLVE}") - set(CMAKE_EXPORT_COMPILE_COMMANDS "ON") - - if (USE_MKL) - find_library(BLAS NAMES libblas.so libblas.dylib PATHS /usr/local/Cellar/lapack/3.9.1_1/lib /usr/lib/x86_64-linux-gnu /usr/lib/i386-linux-gnu /usr/local/Cellar/openblas/0.3.15_1/lib /usr/lib) - find_library(GFORTRAN NAME libgfortran.dylib PATHS /usr/local/Cellar/gcc/10.2.0_4/lib/gcc/10) - find_library(LAPACK NAME liblapack.dylib PATHS /usr/lib) - find_library(OPENMP NAME libiomp5.dylib PATHS /opt/intel/oneapi/compiler/2021.1.1/mac/compiler/lib) - - include_directories (BEFORE ${MKLROOT}/include) - set(PROJECT_LIBS ${BLAS_LIBRARIES} ${LAPACK_LIBRARIES} ${GFORTRAN_LIBRARIES}) - set(MKL_LINK "-L${MKLROOT}/lib -Wl,-rpath,${MKLROOT}/lib -lmkl_intel_ilp64 -lmkl_sequential -lmkl_core -lpthread -lm -ldl") - add_definitions(-DEIGEN_USE_MKL_ALL) - else() - set(MKL_LINK "") - endif(USE_MKL) - - include_directories (BEFORE ../external) - include_directories (BEFORE ../external/minimum_ellipsoid) - include_directories (BEFORE ../include/generators) - include_directories (BEFORE ../include/volume) - include_directories (BEFORE ../include) - include_directories (BEFORE ../include/integration) - include_directories (BEFORE ../include/convex_bodies) - include_directories (BEFORE ../include/convex_bodies/spectrahedra) - include_directories (BEFORE ../include/annealing) - include_directories (BEFORE ../include/ode_solvers) - include_directories (BEFORE ../include/root_finders) - include_directories (BEFORE ../include/samplers) - include_directories (BEFORE ../include/lp_oracles) - include_directories (BEFORE ../include/nlp_oracles) - include_directories (BEFORE ../include/misc) - include_directories (BEFORE ../test) - - #for Eigen - if (${CMAKE_VERSION} VERSION_LESS "3.12.0") - add_compile_options(-D "EIGEN_NO_DEBUG") - else () - add_compile_definitions("EIGEN_NO_DEBUG") - endif () - - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard - add_definitions(${CMAKE_CXX_FLAGS} "-g") # enable debuger - #add_definitions(${CMAKE_CXX_FLAGS} "-Wint-in-bool-context") - #add_definitions(${CMAKE_CXX_FLAGS} "-Wall") - - add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler - #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") - add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") - add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-ldl") - add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-DBOOST_NO_AUTO_PTR") - add_definitions(${CMAKE_CXX_FLAGS} "-DMKL_ILP64") - #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgslcblas") - #add_definitions( "-O3 -lgsl -lm -ldl -lgslcblas" ) - - add_executable (new_volume_example new_volume_example.cpp) - add_executable (benchmarks_sob benchmarks_sob.cpp) - add_executable (benchmarks_cg benchmarks_cg.cpp) - add_executable (benchmarks_cb benchmarks_cb.cpp) - - add_library(test_main OBJECT test_main.cpp) - - add_executable (mcmc_diagnostics_test mcmc_diagnostics_test.cpp $) - add_test(NAME test_psrf COMMAND mcmc_diagnostics_test -tc=psrf) - add_test(NAME test_univariate_psrf COMMAND mcmc_diagnostics_test -tc=univariate_psrf) - add_test(NAME test_interval_psrf COMMAND mcmc_diagnostics_test -tc=interval_psrf) - add_test(NAME test_ess COMMAND mcmc_diagnostics_test -tc=ess) - add_test(NAME test_geweke COMMAND mcmc_diagnostics_test -tc=geweke) - add_test(NAME test_raftery COMMAND mcmc_diagnostics_test -tc=raftery) - - add_executable (sampling_test sampling_test.cpp $) - add_test(NAME test_dikin COMMAND sampling_test -tc=dikin) - add_test(NAME test_john COMMAND sampling_test -tc=john) - add_test(NAME test_vaidya COMMAND sampling_test -tc=vaidya) - add_test(NAME test_brdhr COMMAND sampling_test -tc=brdhr) - add_test(NAME test_bcdhr COMMAND sampling_test -tc=bcdhr) - add_test(NAME test_grdhr COMMAND sampling_test -tc=grdhr) - add_test(NAME test_gbaw COMMAND sampling_test -tc=gbaw) - add_test(NAME test_ghmc COMMAND sampling_test -tc=ghmc) - - add_executable (mmcs_test mmcs_test.cpp $) - add_test(NAME test_mmcs COMMAND mmcs_test -tc=mmcs) - - add_executable (ode_solvers_test ode_solvers_test.cpp $) - add_test(NAME ode_solvers_test_first_order - COMMAND ode_solvers_test -tc=first_order) - add_test(NAME ode_solvers_test_second_order - COMMAND ode_solvers_test -tc=second_order) - - add_executable (root_finders_test root_finders_test.cpp $) - add_test(NAME root_finders_test_root_finders - COMMAND root_finders_test -tc=root_finders) - - #add_executable (benchmarks_crhmc benchmarks_crhmc.cpp ) - #add_executable (benchmarks_crhmc_sampling benchmarks_crhmc_sampling.cpp ) - - add_executable (crhmc_polytope_preparation_test crhmc_polytope_preparation_test.cpp $) - add_test(NAME crhmc_polytope_test_preparation - COMMAND crhmc_polytope_preparation_test -tc=test_preparation_crhmc) - add_test(NAME crhmc_test_fixed_vars - COMMAND crhmc_polytope_preparation_test -tc=test_fixed_vars_crhmc) - add_test(NAME crhmc_test_dep_vars - COMMAND crhmc_polytope_preparation_test -tc=test_dep_vars_crhmc) - add_test(NAME crhmc_test_center_computation - COMMAND crhmc_polytope_preparation_test -tc=test_center_computation) - - add_executable (boundary_oracles_test boundary_oracles_test.cpp $) - add_test(NAME boundary_oracles_test_h_poly_oracles - COMMAND boundary_oracles_test -tc=h_poly_oracles) - - add_executable (volume_sob_hpolytope volume_sob_hpolytope.cpp $) - add_test(NAME volume_sob_hpolytope_cube COMMAND volume_sob_hpolytope -tc=cube) - add_test(NAME volume_sob_hpolytope_cross COMMAND volume_sob_hpolytope -tc=cross) - add_test(NAME volume_sob_hpolytope_birkhoff COMMAND volume_sob_hpolytope -tc=birk) - add_test(NAME volume_sob_hpolytope_prod_simplex COMMAND volume_sob_hpolytope -tc=prod_simplex) - add_test(NAME volume_sob_hpolytope_simplex COMMAND volume_sob_hpolytope -tc=simplex) - add_test(NAME volume_sob_hpolytope_skinny_cube COMMAND volume_sob_hpolytope -tc=skinny_cube) - add_test(NAME volume_sob_hpolytope_cube_overflow COMMAND volume_sob_hpolytope -tc=cube_overflow) - set_property(TEST volume_sob_hpolytope_cube_overflow PROPERTY TIMEOUT 1) - - add_executable (volume_sob_vpolytope volume_sob_vpolytope.cpp $) - add_test(NAME volume_sob_vpolytope_cube COMMAND volume_sob_vpolytope -tc=cube) - add_test(NAME volume_sob_vpolytope_cross COMMAND volume_sob_vpolytope -tc=cross) - add_test(NAME volume_sob_vpolytope_simplex COMMAND volume_sob_vpolytope -tc=simplex) - - add_executable (volume_cg_hpolytope volume_cg_hpolytope.cpp $) - add_test(NAME volume_cg_hpolytope_cube COMMAND volume_cg_hpolytope -tc=cube) - add_test(NAME volume_cg_hpolytope_cross COMMAND volume_cg_hpolytope -tc=cross) - add_test(NAME volume_cg_hpolytope_birkhoff COMMAND volume_cg_hpolytope -tc=birk) - add_test(NAME volume_cg_hpolytope_prod_simplex COMMAND volume_cg_hpolytope -tc=prod_simplex) - add_test(NAME volume_cg_hpolytope_simplex COMMAND volume_cg_hpolytope -tc=simplex) - add_test(NAME volume_cg_hpolytope_skinny_cube COMMAND volume_cg_hpolytope -tc=skinny_cube) - - add_executable (volume_cg_vpolytope volume_cg_vpolytope.cpp $) - add_test(NAME volume_cg_vpolytope_cube COMMAND volume_cg_vpolytope -tc=cube) - add_test(NAME volume_cg_vpolytope_cross COMMAND volume_cg_vpolytope -tc=cross) - add_test(NAME volume_cg_vpolytope_simplex COMMAND volume_cg_vpolytope -tc=simplex) - - add_executable (volume_cb_hpolytope volume_cb_hpolytope.cpp $) - add_test(NAME volume_cb_hpolytope_cube COMMAND volume_cb_hpolytope -tc=cube) - add_test(NAME volume_cb_hpolytope_cross COMMAND volume_cb_hpolytope -tc=cross) - add_test(NAME volume_cb_hpolytope_birkhoff COMMAND volume_cb_hpolytope -tc=birk) - add_test(NAME volume_cb_hpolytope_prod_simplex COMMAND volume_cb_hpolytope -tc=prod_simplex) - add_test(NAME volume_cb_hpolytope_simplex COMMAND volume_cb_hpolytope -tc=simplex) - add_test(NAME volume_cb_hpolytope_skinny_cube COMMAND volume_cb_hpolytope -tc=skinny_cube) - - add_executable (volume_cb_vpolytope volume_cb_vpolytope.cpp $) - add_test(NAME volume_cb_vpolytope_cube COMMAND volume_cb_vpolytope -tc=cube) - add_test(NAME volume_cb_vpolytope_cross COMMAND volume_cb_vpolytope -tc=cross) - add_test(NAME volume_cb_vpolytope_simplex COMMAND volume_cb_vpolytope -tc=simplex) - - add_executable (volume_cb_zonotopes volume_cb_zonotopes.cpp $) - add_test(NAME volume_cb_zonotopes_uniform_zonotopes - COMMAND volume_cb_zonotopes -tc=uniform_zonotopes) - - add_executable (volume_cb_vpoly_intersection_vpoly volume_cb_vpoly_intersection_vpoly.cpp $) - add_test(NAME volume_cb_vpoly_intersection_vpoly_random_vpoly_sphere - COMMAND volume_cb_vpoly_intersection_vpoly -tc=random_vpoly_sphere) - - add_executable (new_rounding_test new_rounding_test.cpp $) - add_test(NAME test_round_min_ellipsoid - COMMAND new_rounding_test -tc=round_min_ellipsoid) - add_test(NAME test_round_max_ellipsoid - COMMAND new_rounding_test -tc=round_max_ellipsoid) - add_test(NAME test_round_svd - COMMAND new_rounding_test -tc=round_svd) - - add_executable (logconcave_sampling_test logconcave_sampling_test.cpp $) - add_test(NAME logconcave_sampling_test_hmc - COMMAND logconcave_sampling_test -tc=hmc) - add_test(NAME logconcave_sampling_test_uld - COMMAND logconcave_sampling_test -tc=uld) - add_test(NAME logconcave_sampling_test_exponential_biomass_sampling - COMMAND logconcave_sampling_test -tc=exponential_biomass_sampling) - add_test(NAME logconcave_sampling_test_nuts_hmc_truncated - COMMAND logconcave_sampling_test -tc=benchmark_nuts_hmc_truncated) - add_test(NAME logconcave_sampling_test_nuts_hmc - COMMAND logconcave_sampling_test -tc=benchmark_nuts_hmc) - - - - add_executable (crhmc_sampling_test crhmc_sampling_test.cpp $) - add_test(NAME crhmc_sampling_test_crhmc - COMMAND crhmc_sampling_test -tc=crhmc) - add_test(NAME crhmc_test_polytope_sampling - COMMAND crhmc_sampling_test -tc=test_polytope_sampling_crhmc) - add_test(NAME crhmc_test_sparse_sampling - COMMAND crhmc_sampling_test -tc=test_sampling_sparse_problem) - add_executable (simple_mc_integration simple_mc_integration.cpp $) - add_test(NAME simple_mc_integration_over_limits - COMMAND simple_mc_integration -tc=rectangle) - add_test(NAME simple_mc_integration_over_cubes - COMMAND simple_mc_integration -tc=cube) - add_test(NAME simple_mc_integration_over_simplices - COMMAND simple_mc_integration -tc=simplex) - add_test(NAME simple_mc_integration_over_product_simplices - COMMAND simple_mc_integration -tc=prod_simplex) - add_test(NAME simple_mc_integration_over_cross_polytopes - COMMAND simple_mc_integration -tc=cross) - add_test(NAME simple_mc_integration_over_birkhoff_polytopes - COMMAND simple_mc_integration -tc=birkhoff) - - add_executable (order_polytope order_polytope.cpp $) - add_test(NAME order_polytope_basics COMMAND order_polytope -tc=basics) - add_test(NAME order_polytope_line_intersect COMMAND order_polytope -tc=line_intersect) - add_test(NAME order_polytope_reflection COMMAND order_polytope -tc=reflection) - add_test(NAME order_polytope_vec_mult COMMAND order_polytope -tc=vec_mult) - - add_executable (matrix_sampling_test sampling_correlation_matrices_test.cpp $) - add_test(NAME test_corre_spectra_classes COMMAND matrix_sampling_test -tc=corre_spectra) - add_test(NAME test_new_ball_uniform COMMAND matrix_sampling_test -tc=new_ball_uniform) - add_test(NAME test_new_billiard_uniform COMMAND matrix_sampling_test -tc=new_billiard_uniform) - add_test(NAME test_new_accelerated_billiard_uniform COMMAND matrix_sampling_test -tc=new_accelerated_billiard_uniform) - add_test(NAME test_new_ball_uniform_MT COMMAND matrix_sampling_test -tc=new_ball_uniform_MT) - add_test(NAME test_new_rdhr_uniform_MT COMMAND matrix_sampling_test -tc=new_rdhr_uniform_MT) - add_test(NAME test_new_billiard_uniform_MT COMMAND matrix_sampling_test -tc=new_billiard_uniform_MT) - add_test(NAME test_new_accelerated_billiard_uniform_MT COMMAND matrix_sampling_test -tc=new_accelerated_billiard_uniform_MT) - - set(ADDITIONAL_FLAGS "-march=native -DSIMD_LEN=0 -DTIME_KEEPING") - - #set_target_properties(benchmarks_crhmc - # PROPERTIES COMPILE_FLAGS ${ADDITIONAL_FLAGS}) - #set_target_properties(benchmarks_crhmc_sampling - # PROPERTIES COMPILE_FLAGS ${ADDITIONAL_FLAGS}) - set_target_properties(crhmc_polytope_preparation_test - PROPERTIES COMPILE_FLAGS ${ADDITIONAL_FLAGS}) - set_target_properties(crhmc_sampling_test - PROPERTIES COMPILE_FLAGS ${ADDITIONAL_FLAGS}) - - TARGET_LINK_LIBRARIES(new_volume_example ${LP_SOLVE} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(new_volume_example ${LP_SOLVE} coverage_config) - TARGET_LINK_LIBRARIES(volume_sob_hpolytope ${LP_SOLVE} coverage_config) - TARGET_LINK_LIBRARIES(volume_sob_vpolytope ${LP_SOLVE} coverage_config) - TARGET_LINK_LIBRARIES(volume_cg_hpolytope ${LP_SOLVE} coverage_config) - TARGET_LINK_LIBRARIES(volume_cg_vpolytope ${LP_SOLVE} coverage_config) - TARGET_LINK_LIBRARIES(volume_cb_hpolytope ${LP_SOLVE} coverage_config) - TARGET_LINK_LIBRARIES(volume_cb_vpolytope ${LP_SOLVE} coverage_config) - TARGET_LINK_LIBRARIES(volume_cb_zonotopes ${LP_SOLVE} coverage_config) - TARGET_LINK_LIBRARIES(volume_cb_vpoly_intersection_vpoly ${LP_SOLVE} coverage_config) - TARGET_LINK_LIBRARIES(volume_cb_vpoly_intersection_vpoly ${LP_SOLVE} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(new_rounding_test ${LP_SOLVE} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(mcmc_diagnostics_test ${LP_SOLVE} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(sampling_test ${LP_SOLVE} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(mmcs_test ${LP_SOLVE} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(benchmarks_sob ${LP_SOLVE} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(benchmarks_cg ${LP_SOLVE} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(benchmarks_cb ${LP_SOLVE} ${MKL_LINK} coverage_config) - #TARGET_LINK_LIBRARIES(benchmarks_crhmc_sampling ${LP_SOLVE} ${MKL_LINK} QD_LIB coverage_config) - #TARGET_LINK_LIBRARIES(benchmarks_crhmc ${LP_SOLVE} ${MKL_LINK} QD_LIB coverage_config) - TARGET_LINK_LIBRARIES(simple_mc_integration ${LP_SOLVE} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(ode_solvers_test ${LP_SOLVE} ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} QD_LIB coverage_config) - TARGET_LINK_LIBRARIES(boundary_oracles_test ${LP_SOLVE} ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(root_finders_test ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(crhmc_polytope_preparation_test ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} QD_LIB coverage_config) - TARGET_LINK_LIBRARIES(logconcave_sampling_test ${LP_SOLVE} ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} coverage_config) - TARGET_LINK_LIBRARIES(crhmc_sampling_test ${LP_SOLVE} ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} QD_LIB coverage_config) - TARGET_LINK_LIBRARIES(order_polytope ${LP_SOLVE} coverage_config) - TARGET_LINK_LIBRARIES(matrix_sampling_test ${LP_SOLVE} ${MKL_LINK} coverage_config) - -endif() From f8db05ccf6694e1f1246fab730061dee1bce323d Mon Sep 17 00:00:00 2001 From: vfisikop Date: Fri, 16 Feb 2024 12:24:10 +0200 Subject: [PATCH 11/35] Upgrade boost from 1.76 to 1.84 --- external/cmake-files/Boost.cmake | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/external/cmake-files/Boost.cmake b/external/cmake-files/Boost.cmake index 48dad5e6f..4c17f3753 100644 --- a/external/cmake-files/Boost.cmake +++ b/external/cmake-files/Boost.cmake @@ -2,10 +2,10 @@ set(BOOST_CMAKE_DIR ${CMAKE_CURRENT_LIST_DIR}) function(GetBoost) find_path(BOOST_DIR NAMES boost PATHS ${BOOST_CMAKE_DIR}/../_deps/boost-src) - if (NOT BOOST_DIR) - - set(BOOST_URL "https://boostorg.jfrog.io/artifactory/main/release/1.76.0/source/boost_1_76_0.tar.bz2" CACHE STRING "Boost download URL") - set(BOOST_URL_SHA256 "f0397ba6e982c4450f27bf32a2a83292aba035b827a5623a14636ea583318c41" CACHE STRING "Boost download URL SHA256 checksum") + if (NOT BOOST_DIR) + + set(BOOST_URL "https://boostorg.jfrog.io/artifactory/main/release/1.84.0/source/boost_1_84_0.tar.bz2" CACHE STRING "Boost download URL") + set(BOOST_URL_SHA256 "cc4b893acf645c9d4b698e9a0f08ca8846aa5d6c68275c14c3e7949c24109454" CACHE STRING "Boost download URL SHA256 checksum") include(FetchContent) set(FETCHCONTENT_BASE_DIR "${BOOST_CMAKE_DIR}/../_deps") From 1703e69d7d88647a8d35ecef91b71e3a60e48e97 Mon Sep 17 00:00:00 2001 From: vfisikop Date: Fri, 16 Feb 2024 12:44:47 +0200 Subject: [PATCH 12/35] Fix ambiguous call to pow for clang, and disable LoadableBlasLib --- external/cmake-files/LPSolve.cmake | 1 + include/diagnostics/effective_sample_size.hpp | 2 +- include/diagnostics/print_diagnostics.hpp | 2 +- 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/external/cmake-files/LPSolve.cmake b/external/cmake-files/LPSolve.cmake index 56bf2ebc2..f8779ace3 100644 --- a/external/cmake-files/LPSolve.cmake +++ b/external/cmake-files/LPSolve.cmake @@ -34,6 +34,7 @@ function(GetLPSolve) add_compile_options(-DLoadLanguageLib=0) add_compile_definitions(RoleIsExternalInvEngine) add_compile_definitions(INVERSE_ACTIVE=3) + add_compile_options(-DLoadableBlasLib=0) include_directories (BEFORE ${LP_SOLVE_DIR}) include_directories (BEFORE ${LP_SOLVE_DIR}/bfp) diff --git a/include/diagnostics/effective_sample_size.hpp b/include/diagnostics/effective_sample_size.hpp index aaf418529..c372eb696 100644 --- a/include/diagnostics/effective_sample_size.hpp +++ b/include/diagnostics/effective_sample_size.hpp @@ -68,7 +68,7 @@ VT effective_sample_size(MT const& samples, unsigned int &min_ess) { variance = NT(0); for (int j = 0; j < N; j++) { - variance += pow(normalized_sample_row[j], 2); + variance += std::pow(normalized_sample_row[j], 2); } variance *= (1.0 / N); diff --git a/include/diagnostics/print_diagnostics.hpp b/include/diagnostics/print_diagnostics.hpp index 91bd8b0ed..b009d6d0c 100644 --- a/include/diagnostics/print_diagnostics.hpp +++ b/include/diagnostics/print_diagnostics.hpp @@ -42,7 +42,7 @@ void print_diagnostics(MT const& samples, unsigned int &min_ess, StreamType &str row_mean = samples.row(i).mean(); row_std = NT(0); for (int j = 0; j < N; j++) { - row_std += pow(samples(i, j) - row_mean, 2); + row_std += std::pow(samples(i, j) - row_mean, 2); } row_std = sqrt(row_std / N); vt.addRow(i + 1, row_mean, row_std, ess(i), intv_psrf(i)); From 5bf9188fb11fddab17de98fabc505e993216ca5f Mon Sep 17 00:00:00 2001 From: vfisikop <160006479+vfisikop@users.noreply.github.com> Date: Tue, 20 Feb 2024 11:38:21 +0200 Subject: [PATCH 13/35] Update requirements.txt --- docs/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/requirements.txt b/docs/requirements.txt index f02db7709..9fa3dfbed 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -2,3 +2,4 @@ breathe sphinx_sitemap myst_parser sphinx-math-dollar +sphinx_rtd_theme From b2b5c06ceb18ea599cdd8cde110197db2f7d024b Mon Sep 17 00:00:00 2001 From: zhanggiene Date: Wed, 6 Mar 2024 16:17:00 +0800 Subject: [PATCH 14/35] Autopoint (#256) --- R-proj/src/Makevars | 2 +- examples/autopoint/CMakeLists.txt | 165 + .../autopoint/Gaussian_mixture_autopoint.cpp | 122 + ...idimensionalGaussian_mixture_autopoint.cpp | 126 + examples/autopoint/README.md | 65 + ..._multidimensional_gaussian_mixture_data.py | 18 + examples/autopoint/readData.cpp | 37 + examples/autopoint/readData.h | 8 + examples/autopoint/requirements.txt | 3 + examples/autopoint/test1.txt | 8000 +++++++++++++++++ .../userDefinedFunction_autopoint.cpp | 111 + .../userDefinedFunction_manual_gradient.cpp | 154 + external/cmake-files/Autodiff.cmake | 31 + include/cartesian_geom/autopoint.h | 311 + include/diagnostics/print_diagnostics.hpp | 2 +- .../generators/known_polytope_generators.h | 6 +- include/ode_solvers/oracle_functors.hpp | 86 + test/CMakeLists.txt | 10 +- 18 files changed, 9251 insertions(+), 6 deletions(-) create mode 100644 examples/autopoint/CMakeLists.txt create mode 100644 examples/autopoint/Gaussian_mixture_autopoint.cpp create mode 100644 examples/autopoint/MultidimensionalGaussian_mixture_autopoint.cpp create mode 100644 examples/autopoint/README.md create mode 100644 examples/autopoint/generate_multidimensional_gaussian_mixture_data.py create mode 100644 examples/autopoint/readData.cpp create mode 100644 examples/autopoint/readData.h create mode 100644 examples/autopoint/requirements.txt create mode 100644 examples/autopoint/test1.txt create mode 100755 examples/autopoint/userDefinedFunction_autopoint.cpp create mode 100644 examples/autopoint/userDefinedFunction_manual_gradient.cpp create mode 100644 external/cmake-files/Autodiff.cmake create mode 100644 include/cartesian_geom/autopoint.h diff --git a/R-proj/src/Makevars b/R-proj/src/Makevars index ea3607acf..12623b8b9 100644 --- a/R-proj/src/Makevars +++ b/R-proj/src/Makevars @@ -1,4 +1,4 @@ -PKG_CPPFLAGS= -I../../external/boost -I../../external/LPsolve_src/run_headers -I../../external/minimum_ellipsoid -I../../include +PKG_CPPFLAGS= -I../../external/boost -I../../external/autodiff -I../../external/LPsolve_src/run_headers -I../../external/minimum_ellipsoid -I../../include PKG_CXXFLAGS= -Wno-deprecated-declarations -lm -ldl -Wno-ignored-attributes -DBOOST_NO_AUTO_PTR -DDISABLE_NLP_ORACLES diff --git a/examples/autopoint/CMakeLists.txt b/examples/autopoint/CMakeLists.txt new file mode 100644 index 000000000..d96e6dac2 --- /dev/null +++ b/examples/autopoint/CMakeLists.txt @@ -0,0 +1,165 @@ +# VolEsti (volume computation and sampling library) +# Copyright (c) 2012-2018 Vissarion Fisikopoulos +# Copyright (c) 2018 Apostolos Chalkis +# Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2020 program. +# Contributed and/or modified by Zhang zhuyan, as part of Google Summer of Code 2022 program. +# Licensed under GNU LGPL.3, see LICENCE file + + +project( VolEsti ) + + +option(DISABLE_NLP_ORACLES "Disable non-linear oracles (used in collocation)" ON) +option(BUILTIN_EIGEN "Use eigen from ../external" OFF) +option(BUILTIN_AUTODIFF "Use eigen from ../external" OFF) +option(USE_MKL "Use MKL library to build eigen" ON) + +CMAKE_MINIMUM_REQUIRED(VERSION 3.17) + +set(MKLROOT /opt/intel/oneapi/mkl/2023.0.0) +message( "MKLROOT" ${MKLROOT}) +set(CMAKE_ALLOW_LOOSE_LOOP_CONSTRUCTS true) + +if(COMMAND cmake_policy) + cmake_policy(SET CMP0003 NEW) +endif(COMMAND cmake_policy) + + +if(DISABLE_NLP_ORACLES) + add_definitions(-DDISABLE_NLP_ORACLES) +else() + find_library(IFOPT NAMES libifopt_core.so PATHS /usr/local/lib) + find_library(IFOPT_IPOPT NAMES libifopt_ipopt.so PATHS /usr/local/lib) + find_library(GMP NAMES libgmp.so PATHS /usr/lib/x86_64-linux-gnu /usr/lib/i386-linux-gnu) + find_library(MPSOLVE NAMES libmps.so PATHS /usr/local/lib) + find_library(PTHREAD NAMES libpthread.so PATHS /usr/lib/x86_64-linux-gnu /usr/lib/i386-linux-gnu) + find_library(FFTW3 NAMES libfftw3.so.3 PATHS /usr/lib/x86_64-linux-gnu /usr/lib/i386-linux-gnu) + + if (NOT IFOPT) + + message(FATAL_ERROR "This program requires the ifopt library, and will not be compiled.") + + elseif (NOT GMP) + + message(FATAL_ERROR "This program requires the gmp library, and will not be compiled.") + + elseif (NOT MPSOLVE) + + message(FATAL_ERROR "This program requires the mpsolve library, and will not be compiled.") + + elseif (NOT FFTW3) + + message(FATAL_ERROR "This program requires the fftw3 library, and will not be compiled.") + + else() + message(STATUS "Library ifopt found: ${IFOPT}") + message(STATUS "Library gmp found: ${GMP}") + message(STATUS "Library mpsolve found: ${MPSOLVE}") + message(STATUS "Library fftw3 found:" ${FFTW3}) + + endif(NOT IFOPT) + +endif(DISABLE_NLP_ORACLES) + +include("../../external/cmake-files/Autodiff.cmake") +GetAutodiff() +include("../../external/cmake-files/Eigen.cmake") +GetEigen() + +include("../../external/cmake-files/Boost.cmake") +GetBoost() + +include("../../external/cmake-files/LPSolve.cmake") +GetLPSolve() + +if (BUILTIN_EIGEN) + include_directories (BEFORE ../../external/_deps/Eigen) +else () + include_directories(BEFORE /usr/include/eigen3) +endif(BUILTIN_EIGEN) +if (BUILTIN_AUTODIFF) + include_directories (BEFORE ../../external/_deps/Autodiff) +else () + include_directories(BEFORE /usr/local/include) +endif(BUILTIN_AUTODIFF) + +if (USE_MKL) +find_library(BLAS +NAMES libblas.so +PATHS /usr/lib/x86_64-linux-gnu /usr/lib/i386-linux-gnu /usr/lib64/atlas /usr/lib/atlas /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib +) + +message("Found MKL: ${MKLROOT}") + include_directories (BEFORE ${MKLROOT}/include) + set(PROJECT_LIBS ${BLAS_LIBRARIES}) + set(MKL_LINK "-L${MKLROOT}/lib -Wl,-rpath,${MKLROOT}/lib -lmkl_intel_ilp64 -lmkl_sequential -lmkl_core -lpthread -lm -ldl") + add_definitions(-DEIGEN_USE_MKL_ALL) +endif(USE_MKL) + +# Find lpsolve library +find_library(LP_SOLVE NAMES liblpsolve55.so PATHS /usr/lib/lp_solve) + +if (NOT LP_SOLVE) + message(FATAL_ERROR "This program requires the lp_solve library, and will not be compiled.") +else () + message(STATUS "Library lp_solve found: ${LP_SOLVE} ${BLAS}") + + set(CMAKE_EXPORT_COMPILE_COMMANDS "ON") + + include_directories (BEFORE ../../external) + include_directories (BEFORE ../../external/minimum_ellipsoid) + include_directories (BEFORE ../../include/generators) + include_directories (BEFORE ../../include/volume) + include_directories (BEFORE ../../include) + include_directories (BEFORE ../../include/lp_oracles) + include_directories (BEFORE ../../include/nlp_oracles) + include_directories (BEFORE ../../include/convex_bodies) + include_directories (BEFORE ../../include/random_walks) + include_directories (BEFORE ../../include/annealing) + include_directories (BEFORE ../../include/ode_solvers) + include_directories (BEFORE ../../include/root_finders) + include_directories (BEFORE ../../include/samplers) + include_directories (BEFORE ../../include/misc) + include_directories (BEFORE ../../include/optimization) + + # for Eigen + if (${CMAKE_VERSION} VERSION_LESS "3.12.0") + add_compile_options(-D "EIGEN_NO_DEBUG") + else () + add_compile_definitions("EIGEN_NO_DEBUG") + endif () + + + message("THIS IS mkl ${MKL_INCLUDE_DIRS}") + if (MKL_FOUND) + message("MKL") + message("${MKL_INCLUDE_DIRS}") +endif() + + + + add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") # enable C++14 standard + add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler + add_definitions(${CMAKE_CXX_FLAGS} "-pthread") # optimization of the compiler + + #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") + add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") + add_definitions(${CMAKE_CXX_FLAGS} "-DMKL_ILP64") + add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-ldl") + add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-DBOOST_NO_AUTO_PTR") + + add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-D_REENTRANT") + #add_definitions( "-O3 -lgsl -lm -ldl -lgslcblas" ) + + add_executable (userDefinedFunction_manual_gradient userDefinedFunction_manual_gradient.cpp) + TARGET_LINK_LIBRARIES(userDefinedFunction_manual_gradient ${LP_SOLVE} ${BLAS} ${MKL_LINK}) + add_executable (userDefinedFunction_autopoint userDefinedFunction_autopoint.cpp) + TARGET_LINK_LIBRARIES(userDefinedFunction_autopoint ${LP_SOLVE} ${BLAS} ${MKL_LINK}) + add_executable (Gaussian_mixture_autopoint Gaussian_mixture_autopoint.cpp readData.cpp) + TARGET_LINK_LIBRARIES(Gaussian_mixture_autopoint ${LP_SOLVE} ${BLAS} ${MKL_LINK}) + add_executable (MultidimensionalGaussian_mixture_autopoint MultidimensionalGaussian_mixture_autopoint.cpp readData.cpp) + TARGET_LINK_LIBRARIES(MultidimensionalGaussian_mixture_autopoint ${LP_SOLVE} ${BLAS} ${MKL_LINK}) + + + endif() + diff --git a/examples/autopoint/Gaussian_mixture_autopoint.cpp b/examples/autopoint/Gaussian_mixture_autopoint.cpp new file mode 100644 index 000000000..b0d4f2b96 --- /dev/null +++ b/examples/autopoint/Gaussian_mixture_autopoint.cpp @@ -0,0 +1,122 @@ +// Use forward-mode automatic differentiation using Autodiff Library + +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2012-2020 Vissarion Fisikopoulos +// Copyright (c) 2018-2020 Apostolos Chalkis +// Copyright (c) 2020-2020 Marios Papachristou +// Copyright (c) 2022-2022 Zhang zhuyan + +// Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2020 program. +// Contributed and/or modified by Zhang zhuyan, as part of Google Summer of Code 2020 program. + +// Licensed under GNU LGPL.3, see LICENCE file + +// task number 1 use array and fix the problem first done +// difference between EigenArray and EigenVector + +#include +#include +#include +#include +#include +#include +#include + +#include "Eigen/Eigen" + +#include "ode_solvers/ode_solvers.hpp" +#include "random.hpp" +#include "random/uniform_int.hpp" +#include "random/normal_distribution.hpp" +#include "random/uniform_real_distribution.hpp" +#include "random_walks/random_walks.hpp" +#include "volume/volume_sequence_of_balls.hpp" +#include "volume/volume_cooling_gaussians.hpp" +#include "volume/volume_cooling_balls.hpp" +#include "generators/known_polytope_generators.h" +#include "readData.h" +#include "diagnostics/diagnostics.hpp" +#include "cartesian_geom/autopoint.h" + +template +void run_main() +{ + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef std::vector pts; + typedef HPolytope Hpolytope; + typedef BoostRandomNumberGenerator RandomNumberGenerator; + typedef AutoDiffFunctor::GradientFunctor NegativeGradientFunctor; + typedef AutoDiffFunctor::FunctionFunctor NegativeLogprobFunctor; + typedef LeapfrogODESolver Solver; + typedef typename Hpolytope::MT MT; + typedef typename Hpolytope::VT VT; + AutoDiffFunctor::parameters params; + params.data = readMatrix("data.txt"); + NegativeGradientFunctor F(params); + NegativeLogprobFunctor f(params); + RandomNumberGenerator rng(1); + unsigned int dim = 2; + + HamiltonianMonteCarloWalk::parameters hmc_params(F, dim); + hmc_params.eta=0.00005;// working learning rate for this specific example + std::chrono::time_point start, stop; + + Hpolytope P = generate_cube(dim, false); + + Point x0 = -0.25 * Point::all_ones(dim); + + // In the first argument put in the address of an H-Polytope + // for truncated sampling and NULL for untruncated + HamiltonianMonteCarloWalk::Walk + hmc(&P, x0, F, f, hmc_params); + int n_samples = 50000; // Half will be burned + int max_actual_draws = n_samples / 2; + unsigned int min_ess = 0; + MT samples; + samples.resize(dim, max_actual_draws); + + for (int i = 0; i < n_samples - max_actual_draws; i++) { + hmc.apply(rng, 3); + } + start = std::chrono::high_resolution_clock::now(); + std::cerr << (long)std::chrono::duration_cast(start - stop).count(); + for (int i = 0; i < max_actual_draws; i++) { + std::cout << hmc.x.getCoefficients().transpose() << std::endl; + hmc.apply(rng, 3); + samples.col(i) = hmc.x.getCoefficients(); + } + stop = std::chrono::high_resolution_clock::now(); + long ETA = (long)std::chrono::duration_cast(stop - start).count(); + std::cerr << "total time taken " << ETA << std::endl; + std::cerr << std::endl; + print_diagnostics(samples, min_ess, std::cerr); + std::cerr << "min ess " << min_ess << "us" << std::endl; + std::cerr << "Average time per sample: " << ETA / max_actual_draws << "us" << std::endl; + std::cerr << "Average time per independent sample: " << ETA / min_ess << "us" << std::endl; + std::cerr << "Average number of reflections: " << (1.0 * hmc.solver->num_reflections) / hmc.solver->num_steps << std::endl; + std::cerr << "Step size (final): " << hmc.solver->eta << std::endl; + std::cerr << "Discard Ratio: " << hmc.discard_ratio << std::endl; + std::cerr << "Average Acceptance Probability: " << exp(hmc.average_acceptance_log_prob) << std::endl; + std::cerr << "PSRF: " << multivariate_psrf(samples) << std::endl; + std::cerr << std::endl; +} +using TT = double; +typedef Eigen::Matrix EigenMatrix; +typename autopoint::FT pdf_(const autopoint &x, const Eigen::Matrix &data_) { + // define your function here, + autopoint data_auto = autopoint(data_); + autopoint result = (((-0.5 * 100 * (data_auto - x.getCoefficients()[0]).pow(2)).exp() + (-0.5 * 100 * (data_auto - x.getCoefficients()[1]).pow(2)).exp())).log(); + + auto y = (result * -1.0).sum(); + return y; +} + +template <> +std::function::FT(const autopoint &, const EigenMatrix&)> AutoDiffFunctor::FunctionFunctor_internal::pdf = pdf_; + +int main() { + run_main(); + return 0; +} diff --git a/examples/autopoint/MultidimensionalGaussian_mixture_autopoint.cpp b/examples/autopoint/MultidimensionalGaussian_mixture_autopoint.cpp new file mode 100644 index 000000000..0db40c544 --- /dev/null +++ b/examples/autopoint/MultidimensionalGaussian_mixture_autopoint.cpp @@ -0,0 +1,126 @@ +// Use forward-mode automatic differentiation using Autodiff Library + +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2012-2020 Vissarion Fisikopoulos +// Copyright (c) 2018-2020 Apostolos Chalkis +// Copyright (c) 2020-2020 Marios Papachristou +// Copyright (c) 2022-2022 Zhang zhuyan + +// Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2020 program. +// Contributed and/or modified by Zhang zhuyan, as part of Google Summer of Code 2020 program. + +// Licensed under GNU LGPL.3, see LICENCE file + + +#include +#include +#include +#include +#include +#include +#include + +#include "Eigen/Eigen" + +#include "ode_solvers/ode_solvers.hpp" +#include "random.hpp" +#include "random/uniform_int.hpp" +#include "random/normal_distribution.hpp" +#include "random/uniform_real_distribution.hpp" +#include "random_walks/random_walks.hpp" +#include "volume/volume_sequence_of_balls.hpp" +#include "volume/volume_cooling_gaussians.hpp" +#include "volume/volume_cooling_balls.hpp" +#include "generators/known_polytope_generators.h" +#include "readData.h" +#include "diagnostics/diagnostics.hpp" + + +template +void run_main() +{ + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef std::vector pts; + typedef HPolytope Hpolytope; + typedef BoostRandomNumberGenerator RandomNumberGenerator; + typedef AutoDiffFunctor::GradientFunctor NegativeGradientFunctor; + typedef AutoDiffFunctor::FunctionFunctor NegativeLogprobFunctor; + typedef LeapfrogODESolver Solver; + typedef typename Hpolytope::MT MT; + typedef typename Hpolytope::VT VT; + AutoDiffFunctor::parameters params; + params.data=readMatrix("data.txt"); + NegativeGradientFunctor F(params); + NegativeLogprobFunctor f(params); + RandomNumberGenerator rng(1); + unsigned int dim = 4; + + HamiltonianMonteCarloWalk::parameters hmc_params(F, dim); + std::chrono::time_point start,stop; + + Hpolytope P = generate_cube(dim, false); + + Point x0 = -0.25 * Point::all_ones(dim); + + // In the first argument put in the address of an H-Polytope + // for truncated sampling and NULL for untruncated + HamiltonianMonteCarloWalk::Walk + hmc(&P, x0, F, f, hmc_params); + int n_samples = 50000; // Half will be burned + int max_actual_draws = n_samples / 2; + unsigned int min_ess = 0; + MT samples; + samples.resize(dim, max_actual_draws); + + for (int i = 0; i < n_samples - max_actual_draws; i++) { + hmc.apply(rng, 3); + } + start = std::chrono::high_resolution_clock::now(); + std::cerr << (long)std::chrono::duration_cast(start - stop).count(); + for (int i = 0; i < max_actual_draws; i++) { + std::cout << hmc.x.getCoefficients().transpose() << std::endl; + hmc.apply(rng, 3); + samples.col(i) = hmc.x.getCoefficients(); + } + stop = std::chrono::high_resolution_clock::now(); + long ETA = (long)std::chrono::duration_cast(stop - start).count(); + std::cerr << "total time taken " << ETA << std::endl; + std::cerr << std::endl; + print_diagnostics(samples, min_ess, std::cerr); + std::cerr << "min ess " << min_ess << "us" << std::endl; + std::cerr << "Average time per sample: " << ETA / max_actual_draws << "us" << std::endl; + std::cerr << "Average time per independent sample: " << ETA / min_ess << "us" << std::endl; + std::cerr << "Average number of reflections: " << (1.0 * hmc.solver->num_reflections) / hmc.solver->num_steps << std::endl; + std::cerr << "Step size (final): " << hmc.solver->eta << std::endl; + std::cerr << "Discard Ratio: " << hmc.discard_ratio << std::endl; + std::cerr << "Average Acceptance Probability: " << exp(hmc.average_acceptance_log_prob) << std::endl; + std::cerr << "PSRF: " << multivariate_psrf(samples) << std::endl; + std::cerr << std::endl; +} +using TT=double; +typedef Eigen::Matrix EigenMatrix; +typename autopoint::FT pdf_(const autopoint& x,const EigenMatrix& data_) { + int d=x.getCoefficients().rows(); + autopoint firstX=x.head(d/2); + autopoint secondX=x.tail(d/2); + typename autopoint::FT SUM=0; + auto I=Eigen::Matrix::Identity(d,d); + for(int i=0;i datavector=data_.row(i).transpose(); + autopoint data_autopoint= autopoint(datavector); + autopoint x_mu_first= data_autopoint - firstX; + autopoint x_mu_second= data_autopoint -secondX; + SUM +=(-log(exp(-0.5*x_mu_first.pow(2).sum()) + exp(-0.5*x_mu_second.pow(2).sum() ) )); + } + return SUM; +} +template <> std::function::FT(const autopoint&,const EigenMatrix&)> AutoDiffFunctor::FunctionFunctor_internal::pdf=pdf_; + + + +int main() { + run_main(); + return 0; +} diff --git a/examples/autopoint/README.md b/examples/autopoint/README.md new file mode 100644 index 000000000..d49ffd8ea --- /dev/null +++ b/examples/autopoint/README.md @@ -0,0 +1,65 @@ + +## Background information +Autopoint is implemented using [Autodiff Library]( +https://github.com/autodiff/autodiff/), after experiments of comparing various automatic differentiating libraries.Read [here](https://gist.github.com/zhanggiene/8471601fa25ba9db90303661b0e2237b) to find out more! + +## Compilation +``` +cmake . +build +``` + +## Running the examples + +For plotting the results: + * `examples/python_utilities/plot_samples.py` for plotting the outputs of the HMC sampler + +The script has requirements listed in `requirements.txt`. Install via +``` +pip install -r requirements.txt +``` + +You can also use the `--limits` argument to set {x, y, z}-limits for the joint plots. + + +### 1. User defined function where the pdf function is explicitly defined by the user. + +``` +./userDefinedFunction_autopoint > userDefinedFunction_autopoint.txt + +python ../python_utilities/plot_samples.py < userDefinedFunction_autopoint.txt +``` +The histogram of samples from user defined distribution.It is the same as the real pdf of the distribution when -1 data.txt +``` +2. Data from the true distribution is used to define the pdf. +``` +./Gaussian_mixture_autopoint > Gaussian_mixture.txt +``` +3. Plot the graph +``` +python ../python_utilities/plot_samples.py < Gaussian_mixture.txt +``` +![gaussian_mixture](./2.2.png) + +### 3.The pdf is defined using a mixture of multi-dimensional gaussian distribution with multi-dimensional data. + +1. Generating data points from a mixture of multi-dimensional gaussian distribution with mean of dimension d. The variable d can be changed to be any integer number, but it should be the same as unsigned int dim variable in MultidimensionalGaussian_mixture_autopoint.cpp +``` +python generate_multidimensional_gaussian_mixture_data.py -d=4 > data.txt +``` +2. Conduct hmc sampling + +``` +./MultidimensionalGaussian_mixture_autopoint +``` +### Points to take note +it is advised to use NutsHamiltonianMonteCarloWalk instead of HamiltonianMonteCarloWalk. diff --git a/examples/autopoint/generate_multidimensional_gaussian_mixture_data.py b/examples/autopoint/generate_multidimensional_gaussian_mixture_data.py new file mode 100644 index 000000000..011957d23 --- /dev/null +++ b/examples/autopoint/generate_multidimensional_gaussian_mixture_data.py @@ -0,0 +1,18 @@ +import numpy as np +import random +import argparse + +argParser = argparse.ArgumentParser() +argParser.add_argument("-d", "--dimension", type=int,help="dimension of mean of normal distribution.There will be samples from two normal distribution") +argParser.add_argument("-n", "--number", type=int,default=4000,help="number of samples") +args = argParser.parse_args() +mean1_=[random.random() for i in range(args.dimension) ] +#the two mean are directly opposite for maximum sampling efficiency +mean2_=[-i for i in mean1_] +cov = 0.1*np.identity(args.dimension) +pts1 = np.random.multivariate_normal(mean1_, cov, size=args.number//2) +pts2 = np.random.multivariate_normal(mean2_, cov, size=args.number//2) +sample=np.r_[pts1,pts2] +for i in sample: + print(" ".join(map(str, i))) + diff --git a/examples/autopoint/readData.cpp b/examples/autopoint/readData.cpp new file mode 100644 index 000000000..d6de0b497 --- /dev/null +++ b/examples/autopoint/readData.cpp @@ -0,0 +1,37 @@ +#include +#include +#include +#include + +using namespace std; +using namespace Eigen; + +#define MAXBUFSIZE ((int)1e6) + +template +Matrix readMatrix(const char * filename) { + int cols=0, rows=0; + NT buff[MAXBUFSIZE]; + + ifstream infile; + infile.open(filename); + while(!infile.eof()) { + string line; + getline(infile,line); + int temp_cols=0; + stringstream stream(line); + while(!stream.eof()) stream>>buff[cols*rows+temp_cols++]; + if(temp_cols==0) continue; + if (cols==0) cols=temp_cols; + rows++; + } + infile.close(); + Matrix result(rows,cols); + for(int i =0;i +#include "readData.cpp" +template +Eigen::Matrix readMatrix(const char * filename); + +#endif /* BCC3CEE0_8697_484B_B5E2_2A7DAFF1284D */ diff --git a/examples/autopoint/requirements.txt b/examples/autopoint/requirements.txt new file mode 100644 index 000000000..010503e45 --- /dev/null +++ b/examples/autopoint/requirements.txt @@ -0,0 +1,3 @@ +matplotlib==3.3.2 +numpy==1.22.0 +seaborn==0.11.0 diff --git a/examples/autopoint/test1.txt b/examples/autopoint/test1.txt new file mode 100644 index 000000000..928bebb1e --- /dev/null +++ b/examples/autopoint/test1.txt @@ -0,0 +1,8000 @@ +1.4389312743620264 +-1.3429444686350107 +-1.2839824846615373 +0.17425408483408453 +-0.317145414150371 +1.210417765935969 +2.039815431693488 +-0.5586309991920968 +0.25595721573618835 +0.4125979094251532 +0.11088989514699005 +-1.627890496582001 +-0.3648803571858281 +-1.2187669402757682 +-0.64164859953216 +-0.14014893845852533 +-2.192549685553632 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+0.9263060329538959 +-1.0803739371976238 +0.4726286931326179 diff --git a/examples/autopoint/userDefinedFunction_autopoint.cpp b/examples/autopoint/userDefinedFunction_autopoint.cpp new file mode 100755 index 000000000..9762bd751 --- /dev/null +++ b/examples/autopoint/userDefinedFunction_autopoint.cpp @@ -0,0 +1,111 @@ +// Use forward-mode automatic differentiation using Autodiff Library +// sampling from log-density equal to f(x) = x^4 -2 * x^2; +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2012-2020 Vissarion Fisikopoulos +// Copyright (c) 2018-2020 Apostolos Chalkis +// Copyright (c) 2020-2020 Marios Papachristou +// Copyright (c) 2022-2022 Zhang zhuyan + +// Contributed and/or modified by Zhang zhuyan, as part of Google Summer of Code 2022 program. + +// Licensed under GNU LGPL.3, see LICENCE file + +#include +#include +#include +#include +#include +#include +#include + +#include "Eigen/Eigen" + +#include "ode_solvers/ode_solvers.hpp" +#include "random.hpp" +#include "random/uniform_int.hpp" +#include "random/normal_distribution.hpp" +#include "random/uniform_real_distribution.hpp" +#include "random_walks/random_walks.hpp" +#include "volume/volume_sequence_of_balls.hpp" +#include "volume/volume_cooling_gaussians.hpp" +#include "volume/volume_cooling_balls.hpp" +#include "generators/known_polytope_generators.h" +#include +#include +#include "diagnostics/diagnostics.hpp" + +template +void run_main() +{ + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef std::vector pts; + typedef HPolytope Hpolytope; + typedef BoostRandomNumberGenerator RandomNumberGenerator; + typedef AutoDiffFunctor::GradientFunctor NegativeGradientFunctor; + typedef AutoDiffFunctor::FunctionFunctor NegativeLogprobFunctor; + typedef LeapfrogODESolver Solver; + typedef typename Hpolytope::MT MT; + typedef typename Hpolytope::VT VT; + + AutoDiffFunctor::parameters params; + + NegativeGradientFunctor F(params); + NegativeLogprobFunctor f(params); + RandomNumberGenerator rng(1); + unsigned int dim = 2; + + HamiltonianMonteCarloWalk::parameters hmc_params(F, dim); + + Hpolytope P = generate_cube(dim, false); + + Point x0 = -0.25 * Point::all_ones(dim); + + // In the first argument put in the address of an H-Polytope + // for truncated sampling and NULL for untruncated + HamiltonianMonteCarloWalk::Walk + hmc(&P, x0, F, f, hmc_params); + std::chrono::time_point start, stop; + int n_samples = 50000; // Half will be burned + int max_actual_draws = n_samples / 2; + unsigned int min_ess = 0; + MT samples; + samples.resize(dim, max_actual_draws); + + for (int i = 0; i < n_samples - max_actual_draws; i++) { + hmc.apply(rng, 3); + } + start = std::chrono::high_resolution_clock::now(); + for (int i = 0; i < max_actual_draws; i++) { + std::cout << hmc.x.getCoefficients().transpose() << std::endl; + hmc.apply(rng, 3); + samples.col(i) = hmc.x.getCoefficients(); + } + stop = std::chrono::high_resolution_clock::now(); + long ETA = (long)std::chrono::duration_cast(stop - start).count(); + std::cerr << "total time taken " << ETA << std::endl; + std::cerr << std::endl; + print_diagnostics(samples, min_ess, std::cerr); + std::cerr << "min ess " << min_ess << "us" << std::endl; + std::cerr << "Average time per sample: " << ETA / max_actual_draws << "us" << std::endl; + std::cerr << "Average time per independent sample: " << ETA / min_ess << "us" << std::endl; + std::cerr << "Average number of reflections: " << (1.0 * hmc.solver->num_reflections) / hmc.solver->num_steps << std::endl; + std::cerr << "Step size (final): " << hmc.solver->eta << std::endl; + std::cerr << "PSRF: " << multivariate_psrf(samples) << std::endl; + std::cerr << std::endl; +} +using TT=double; +typedef Eigen::Matrix EigenMatrix; +typename autopoint::FT pdf_(const autopoint& x,const EigenMatrix& data_) { + // define your function here, + return x.pow(4).sum() - 2 * x.pow(2).sum() + (autopoint::FT)(2 * 0.795); +} + +template <> std::function::FT(const autopoint&,const EigenMatrix&)> AutoDiffFunctor::FunctionFunctor_internal::pdf=pdf_; + +int main() { + run_main(); + + return 0; +} diff --git a/examples/autopoint/userDefinedFunction_manual_gradient.cpp b/examples/autopoint/userDefinedFunction_manual_gradient.cpp new file mode 100644 index 000000000..d4d40b10d --- /dev/null +++ b/examples/autopoint/userDefinedFunction_manual_gradient.cpp @@ -0,0 +1,154 @@ +// Use manual gradient function in existing sampling method +// sampling from log-density equal to f(x) = x^4 -2 * x^2; + +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2012-2020 Vissarion Fisikopoulos +// Copyright (c) 2018-2020 Apostolos Chalkis +// Copyright (c) 2020-2020 Marios Papachristou +// Copyright (c) 2022-2022 Zhang zhuyan + +// Licensed under GNU LGPL.3, see LICENCE file + +#include +#include +#include +#include +#include +#include +#include + +#include "Eigen/Eigen" + +#include "ode_solvers/ode_solvers.hpp" +#include "random.hpp" +#include "random/uniform_int.hpp" +#include "random/normal_distribution.hpp" +#include "random/uniform_real_distribution.hpp" +#include "random_walks/random_walks.hpp" +#include "volume/volume_sequence_of_balls.hpp" +#include "volume/volume_cooling_gaussians.hpp" +#include "volume/volume_cooling_balls.hpp" +#include "generators/known_polytope_generators.h" +#include "diagnostics/diagnostics.hpp" + +struct CustomFunctor { + + template < + typename NT> + struct parameters { + unsigned int order; + NT L; // Lipschitz constant for gradient + NT m; // Strong convexity constant + NT kappa; // Condition number + + parameters() : order(2), L(4), m(4), kappa(1){}; + }; + + template < + typename Point> + struct GradientFunctor { + typedef typename Point::FT NT; + typedef std::vector pts; + + parameters ¶ms; + + GradientFunctor(parameters ¶ms_) : params(params_){}; + + // The index i represents the state vector index + Point operator()(unsigned int const &i, pts const &xs, NT const &t) const { + if (i == params.order - 1) { + auto temp = xs[0].getCoefficients().array(); + auto result = -4 * temp.pow(3) + 4 * temp; + Point y(result); + return y; + } + else { + return xs[i + 1]; // returns derivative + } + } + }; + + template < + typename Point> + struct FunctionFunctor { + typedef typename Point::FT NT; + + parameters ¶ms; + + FunctionFunctor(parameters ¶ms_) : params(params_){}; + + // The index i represents the state vector index + NT operator()(Point const &x) const { + auto temp = x.getCoefficients().array(); + NT y = temp.pow(4).sum() - 2 * temp.pow(2).sum() + 2 * 0.795; + return y; + } + }; +}; + +template +void run_main() { + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef std::vector pts; + typedef HPolytope Hpolytope; + typedef BoostRandomNumberGenerator RandomNumberGenerator; + typedef CustomFunctor::GradientFunctor NegativeGradientFunctor; + typedef CustomFunctor::FunctionFunctor NegativeLogprobFunctor; + typedef LeapfrogODESolver Solver; + + typedef typename Hpolytope::MT MT; + typedef typename Hpolytope::VT VT; + CustomFunctor::parameters params; + + NegativeGradientFunctor F(params); + NegativeLogprobFunctor f(params); + + RandomNumberGenerator rng(1); + unsigned int dim = 2; + + HamiltonianMonteCarloWalk::parameters hmc_params(F, dim); + + Hpolytope P = generate_cube(dim, false); + + Point x0 = -0.25 * Point::all_ones(dim); + + // In the first argument put in the address of an H-Polytope + // for truncated sampling and NULL for untruncated + HamiltonianMonteCarloWalk::Walk + hmc(&P, x0, F, f, hmc_params); + std::chrono::time_point start, stop; + + int n_samples = 50000; // Half will be burned + int max_actual_draws = n_samples / 2; + unsigned int min_ess = 0; + MT samples; + samples.resize(dim, max_actual_draws); + + for (int i = 0; i < n_samples - max_actual_draws; i++) { + hmc.apply(rng, 3); + } + start = std::chrono::high_resolution_clock::now(); + for (int i = 0; i < max_actual_draws; i++) { + std::cout << hmc.x.getCoefficients().transpose() << std::endl; + hmc.apply(rng, 3); + samples.col(i) = hmc.x.getCoefficients(); + } + stop = std::chrono::high_resolution_clock::now(); + long ETA = (long)std::chrono::duration_cast(stop - start).count(); + std::cerr << "total time taken " << ETA << std::endl; + std::cerr << std::endl; + print_diagnostics(samples, min_ess, std::cerr); + std::cerr << "min ess " << min_ess << "us" << std::endl; + std::cerr << "Average time per sample: " << ETA / max_actual_draws << "us" << std::endl; + std::cerr << "Average time per independent sample: " << ETA / min_ess << "us" << std::endl; + std::cerr << "Average number of reflections: " << (1.0 * hmc.solver->num_reflections) / hmc.solver->num_steps << std::endl; + std::cerr << "Step size (final): " << hmc.solver->eta << std::endl; + std::cerr << "PSRF: " << multivariate_psrf(samples) << std::endl; +} + +int main() { + run_main(); + return 0; +} diff --git a/external/cmake-files/Autodiff.cmake b/external/cmake-files/Autodiff.cmake new file mode 100644 index 000000000..06e593548 --- /dev/null +++ b/external/cmake-files/Autodiff.cmake @@ -0,0 +1,31 @@ +set(AUTODIFF_CMAKE_DIR ${CMAKE_CURRENT_LIST_DIR}) +function(GetAutodiff) + find_path(AUTODIFF_DIR NAMES autodiff PATHS ${AUTODIFF_CMAKE_DIR}/../_deps/autodiff-src) + + if (NOT AUTODIFF_DIR ) + include(FetchContent) + set(FETCHCONTENT_BASE_DIR "${AUTODIFF_CMAKE_DIR}/../_deps") + FetchContent_Declare(autodiff + GIT_REPOSITORY https://github.com/autodiff/autodiff.git + GIT_TAG v0.6.12 + ) + + FetchContent_GetProperties(autodiff) + + if(NOT autodiff_POPULATED) + message(STATUS "Autodiff library not found locally, downloading it.") + FetchContent_Populate(autodiff) + endif() + + set(AUTODIFF_DIR ${autodiff_SOURCE_DIR}) + message(STATUS "Using downloaded Autodiff library at: ${AUTODIFF_DIR}") + + else () + + message(STATUS "Autodiff Library found: ${AUTODIFF_DIR}") + + endif() + + include_directories(${AUTODIFF_DIR}) + +endfunction() \ No newline at end of file diff --git a/include/cartesian_geom/autopoint.h b/include/cartesian_geom/autopoint.h new file mode 100644 index 000000000..e1d73f222 --- /dev/null +++ b/include/cartesian_geom/autopoint.h @@ -0,0 +1,311 @@ +#ifndef AB9D4FD9_C418_44AC_8276_CC42540EF027 +#define AB9D4FD9_C418_44AC_8276_CC42540EF027 +#include +#include + +template +class autopoint +{ + public: + unsigned int d; + Eigen::Matrix, Eigen::Dynamic,1> coeffs; + typedef typename std::vector>::iterator iter; + typedef typename autodiff::detail::Real<1, T> FT; + typedef Eigen::Matrix coeff; + typedef Eigen::Matrix, Eigen::Dynamic,1> Coeff; + autopoint() {} + + autopoint(const unsigned int dim) + { + d = dim; + coeffs.setZero(d); + } + + autopoint(const unsigned int dim, iter begin, iter endit) + { + d = dim; + coeffs.resize(d); + int i = 0; + + for (iter it=begin ; it != endit ; it++) + coeffs(i++) = *it; + } + FT operator()(int i,int j) + { + return coeffs(i,j); + } + + autopoint(const Coeff& coeffs) + { + d = coeffs.rows(); + this->coeffs = coeffs; + } + + autopoint(const coeff& coeffs) + { + d = coeffs.rows(); + // Coeff temp= coeffs; + this->coeffs = (Coeff)coeffs; + } + + + autopoint(const unsigned int dim, std::vector> cofs) + { + d = dim; + coeffs.resize(d); + iter it = cofs.begin(); + int i=0; + + for (; it != cofs.end(); it++,i++) + coeffs(i) = *it; + + } + + void add(const Coeff& coeffs) + { + this->coeffs += coeffs; + } + + const Coeff& getCoefficients() const + { + return coeffs; + } + + int dimension() const + { + return d; + } + + void set_dimension(const unsigned int dim) + { + d = dim; + coeffs.setZero(d); + } + + void set_coord(const unsigned int i, FT coord) + { + coeffs(i) = coord; + } + + void set_coeffs (const Coeff& coeffs2) { + d = coeffs2.rows(); + coeffs = coeffs2; + } + + void set_to_origin() { + coeffs.setZero(d); + } + + FT operator[] (const unsigned int i) const + { + return coeffs(i); + } + + FT* pointerToData() + { + return coeffs.data(); + } + + FT sum() const { + return coeffs.sum(); + } + autopoint head(int n) const + { + return autopoint((Coeff)coeffs.head(n)); + } + + autopoint tail(int n) const + { + return autopoint((Coeff)coeffs.tail(n)); + } + + autopoint pow(int n) const { + autopoint temp; + temp.d = d; + temp.coeffs=coeffs.array().pow(n).matrix(); + return temp; + } + + + autopoint log() const { + autopoint temp; + temp.d = d; + temp.coeffs=coeffs.array().log().matrix(); + return temp; + } + + autopoint exp() const { + autopoint temp; + temp.d = d; + temp.coeffs=coeffs.array().exp().matrix(); + return temp; + } + void operator+= (const autopoint& p) + { + coeffs += p.getCoefficients(); + } + + void operator+= (const Coeff& coeffs) + { + this->coeffs += coeffs ; + } + + void operator-= (const autopoint& p) + { + coeffs -= p.getCoefficients(); + } + + void operator-= (const Coeff& coeffs) + { + this->coeffs -= coeffs ; + } + + + void operator= (const Coeff& coeffs) + { + this->coeffs = coeffs; + d = coeffs.rows(); + } + + // copy assignment + autopoint& operator=(const autopoint& coeffs) + { + // Guard self assignment + if (this == &coeffs) + return *this; + this->coeffs = coeffs; + d = coeffs.rows(); + return *this; + } + autopoint transpose() const + { + return autopoint((Coeff)coeffs.transpose()); + } + + autopoint operator+ (const autopoint& p) const + { + return autopoint((Coeff)(coeffs+p.getCoefficients())); + } + autopoint operator- (const autopoint& p) const + { + return autopoint((Coeff)(coeffs-p.getCoefficients())); + } + + autopoint operator- (const FT p) const + { + autopoint temp_auto; + temp_auto.d=d; + auto temp=coeffs.array(); + temp_auto.coeffs=(temp-p).matrix(); + return temp_auto; + } + autopoint operator- (T p) const + { + autopoint temp_auto; + temp_auto.d=d; + auto temp=coeffs.array(); + temp_auto.coeffs=(temp-p).matrix(); + return temp_auto; + } + + autopoint operator* (const FT k) + { + return autopoint(coeffs * k); + } + + autopoint operator* (T k) const + { + return autopoint((Coeff)(coeffs * ((FT)k))); + } + + autopoint operator* (T k) + { + return autopoint((Coeff)(coeffs * ((FT)k))); + } + // matrix multiplication + autopoint operator* (const autopoint& autopoint_) + { + return autopoint((Coeff)(coeffs * autopoint_.getCoefficients())); + } + // matrix multiplication with normal matrix + autopoint operator* (const coeff& matrix_) + { + return autopoint(( autopoint(matrix_) * autopoint(coeffs) )); + } + + void operator*= (const FT k) + { + coeffs *= k; + } + + void operator*= (const T k) + { + FT k_=k; + coeffs *= k_; + } + + + void operator/= (const FT k) + { + coeffs /= k; + } + + + FT distance(const autopoint& p) const + { + return (this->coeffs - p.coeffs).norm(); + } + + FT dot(const autopoint& p) const + { + return coeffs.dot(p.getCoefficients()); + } + + FT dot(const Coeff& coeffs) const + { + return this->coeffs.dot(coeffs); + } + + FT squared_length() const { + FT lsq = length(); + return lsq * lsq; + } + + FT length() const { + return coeffs.norm(); + } + + void print() const + { + for(unsigned int i=0; i +autopoint operator* ( K k, autopoint const& p) +{ + return p * k; +} + +// matrix times autopoint +template +autopoint operator* ( Eigen::Matrix matrix_, autopoint const& p) +{ + Eigen::Matrix, Eigen::Dynamic,1> temp= matrix_*p.getCoefficients(); + return autopoint(temp); +} + + + +#endif /* AB9D4FD9_C418_44AC_8276_CC42540EF027 */ diff --git a/include/diagnostics/print_diagnostics.hpp b/include/diagnostics/print_diagnostics.hpp index b009d6d0c..9e27f0bc7 100644 --- a/include/diagnostics/print_diagnostics.hpp +++ b/include/diagnostics/print_diagnostics.hpp @@ -49,7 +49,7 @@ void print_diagnostics(MT const& samples, unsigned int &min_ess, StreamType &str } vt.print(stream); - std::cout << "interval_psrf = " << intv_psrf.maxCoeff() << "us" << std::endl; + stream << "interval_psrf = " << intv_psrf.maxCoeff() << "us" << std::endl; } diff --git a/include/generators/known_polytope_generators.h b/include/generators/known_polytope_generators.h index 050918ad0..8d014621d 100644 --- a/include/generators/known_polytope_generators.h +++ b/include/generators/known_polytope_generators.h @@ -17,7 +17,7 @@ /// The result can be either in V-representation (Vpoly=true) or in H-representation (V-poly-false) /// @tparam Polytope Type of returned polytope template -Polytope generate_cube(const unsigned int& dim, const bool& Vpoly) { +Polytope generate_cube(const unsigned int& dim, const bool& Vpoly,typename Polytope::NT scale=1) { typedef typename Polytope::MT MT; typedef typename Polytope::VT VT; @@ -30,7 +30,7 @@ Polytope generate_cube(const unsigned int& dim, const bool& Vpoly) { A.resize(2 * dim, dim); b.resize(2 * dim); for (unsigned int i = 0; i < dim; ++i) { - b(i) = 1.0; + b(i) = scale; for (unsigned int j = 0; j < dim; ++j) { if (i == j) { A(i, j) = 1.0; @@ -40,7 +40,7 @@ Polytope generate_cube(const unsigned int& dim, const bool& Vpoly) { } } for (unsigned int i = 0; i < dim; ++i) { - b(i + dim) = 1.0; + b(i + dim) = scale; for (unsigned int j = 0; j < dim; ++j) { if (i == j) { A(i + dim, j) = -1.0; diff --git a/include/ode_solvers/oracle_functors.hpp b/include/ode_solvers/oracle_functors.hpp index 35fc8887b..80da6d425 100644 --- a/include/ode_solvers/oracle_functors.hpp +++ b/include/ode_solvers/oracle_functors.hpp @@ -10,6 +10,12 @@ #ifndef ODE_SOLVERS_ORACLE_FUNCTORS_HPP #define ODE_SOLVERS_ORACLE_FUNCTORS_HPP +#include "Eigen/Eigen" +#include +#include +#include "cartesian_geom/cartesian_kernel.h" +#include "cartesian_geom/autopoint.h" + struct OptimizationFunctor { template < @@ -394,4 +400,84 @@ struct HessianFunctor { }; +struct AutoDiffFunctor { +template < + typename NT> +struct parameters { + unsigned int order; + NT L; + // Lipschitz constant for gradient + NT m; + // Strong convexity constant + NT kappa; // Condition number + Eigen::Matrix data; + parameters() : order(2), L(4), m(4), kappa(1){}; +}; + +template < + typename NT> +struct FunctionFunctor_internal { + using Autopoint =autopoint; + using Coeff=typename autopoint::Coeff; + using FT =typename autopoint::FT; + using Kernel=Cartesian; + using Point=typename Kernel::Point; + static std::function &)> pdf; + // static std::function f; + FT static result_internal(const Coeff &x, const Eigen::Matrix &data){ + return pdf(x, data); // + } + // external interface + Point static differentiate(Point const &x0, const Eigen::Matrix &data) { + Autopoint x = Autopoint(x0.getCoefficients()); // cast into autopoint + auto x1 = x.getCoefficients(); + Coeff y = autodiff::gradient(result_internal, autodiff::wrt(x1), autodiff::at(x1, data)); + auto result = y.template cast(); + return -1 * Point(result); + } + + NT static result(Point const &x0, const Eigen::Matrix &data) { + Autopoint x = Autopoint(x0.getCoefficients()); // cast to autopoint + auto x1 = x.getCoefficients(); + return result_internal(x1, data).val(); + } +}; + +template < + typename Point> +struct GradientFunctor { + typedef typename Point::FT NT; + typedef std::vector pts; + typedef FunctionFunctor_internal NegativeLogprobFunctor; + NegativeLogprobFunctor F; + parameters ¶ms; + GradientFunctor(parameters ¶ms_) : params(params_){}; + // The index i represents the state vector index + Point operator()(unsigned int const &i, pts const &xs, NT const &t) const { + // std::cout<<"calling gradient functor"< +struct FunctionFunctor { + typedef typename Point::FT NT; + parameters ¶ms; + FunctionFunctor(parameters ¶ms_) : params(params_){}; + // The index i represents the state vector index + typedef FunctionFunctor_internal NegativeLogprobFunctor; + NegativeLogprobFunctor F; + NT operator()(Point const &x) const { + NT temp = F.result(x, params.data); + // std::cout< Date: Wed, 6 Mar 2024 13:02:29 +0200 Subject: [PATCH 15/35] Enable c++17 support in tests and fix saxpy calls (#292) * Enable c++17 support in tests and fix saxpy calls * Fix structure, code style and tests in autodiff --- examples/autopoint/CMakeLists.txt | 18 ++-- .../autopoint/Gaussian_mixture_autopoint.cpp | 1 + ...idimensionalGaussian_mixture_autopoint.cpp | 3 +- .../userDefinedFunction_autopoint.cpp | 3 +- .../userDefinedFunction_manual_gradient.cpp | 3 +- examples/correlation_matrices/CMakeLists.txt | 11 ++- .../CMakeLists.txt | 2 - include/cartesian_geom/autopoint.h | 28 ++++-- .../ode_solvers/oracle_autodiff_functors.hpp | 94 +++++++++++++++++++ include/ode_solvers/oracle_functors.hpp | 86 ----------------- include/preprocess/crhmc/crhmc_utils.h | 2 +- include/preprocess/crhmc/two_sided_barrier.h | 4 +- .../crhmc/weighted_two_sided_barrier.h | 4 +- test/CMakeLists.txt | 1 + 14 files changed, 146 insertions(+), 114 deletions(-) create mode 100644 include/ode_solvers/oracle_autodiff_functors.hpp diff --git a/examples/autopoint/CMakeLists.txt b/examples/autopoint/CMakeLists.txt index d96e6dac2..77e592dd0 100644 --- a/examples/autopoint/CMakeLists.txt +++ b/examples/autopoint/CMakeLists.txt @@ -11,8 +11,7 @@ project( VolEsti ) option(DISABLE_NLP_ORACLES "Disable non-linear oracles (used in collocation)" ON) option(BUILTIN_EIGEN "Use eigen from ../external" OFF) -option(BUILTIN_AUTODIFF "Use eigen from ../external" OFF) -option(USE_MKL "Use MKL library to build eigen" ON) +option(USE_MKL "Use MKL library to build eigen" OFF) CMAKE_MINIMUM_REQUIRED(VERSION 3.17) @@ -61,8 +60,15 @@ else() endif(DISABLE_NLP_ORACLES) +option(BUILTIN_AUTODIFF "Use autodiff from ../external" OFF) include("../../external/cmake-files/Autodiff.cmake") GetAutodiff() +if (BUILTIN_AUTODIFF) + include_directories (BEFORE ../../external/_deps/Autodiff) +else () + include_directories(BEFORE /usr/local/include) +endif(BUILTIN_AUTODIFF) + include("../../external/cmake-files/Eigen.cmake") GetEigen() @@ -77,11 +83,7 @@ if (BUILTIN_EIGEN) else () include_directories(BEFORE /usr/include/eigen3) endif(BUILTIN_EIGEN) -if (BUILTIN_AUTODIFF) - include_directories (BEFORE ../../external/_deps/Autodiff) -else () - include_directories(BEFORE /usr/local/include) -endif(BUILTIN_AUTODIFF) + if (USE_MKL) find_library(BLAS @@ -141,7 +143,7 @@ endif() add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") # enable C++14 standard add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler add_definitions(${CMAKE_CXX_FLAGS} "-pthread") # optimization of the compiler - + #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") add_definitions(${CMAKE_CXX_FLAGS} "-DMKL_ILP64") diff --git a/examples/autopoint/Gaussian_mixture_autopoint.cpp b/examples/autopoint/Gaussian_mixture_autopoint.cpp index b0d4f2b96..44dbbd539 100644 --- a/examples/autopoint/Gaussian_mixture_autopoint.cpp +++ b/examples/autopoint/Gaussian_mixture_autopoint.cpp @@ -26,6 +26,7 @@ #include "Eigen/Eigen" #include "ode_solvers/ode_solvers.hpp" +#include "ode_solvers/oracle_autodiff_functors.hpp" #include "random.hpp" #include "random/uniform_int.hpp" #include "random/normal_distribution.hpp" diff --git a/examples/autopoint/MultidimensionalGaussian_mixture_autopoint.cpp b/examples/autopoint/MultidimensionalGaussian_mixture_autopoint.cpp index 0db40c544..432b7141a 100644 --- a/examples/autopoint/MultidimensionalGaussian_mixture_autopoint.cpp +++ b/examples/autopoint/MultidimensionalGaussian_mixture_autopoint.cpp @@ -24,6 +24,7 @@ #include "Eigen/Eigen" #include "ode_solvers/ode_solvers.hpp" +#include "ode_solvers/oracle_autodiff_functors.hpp" #include "random.hpp" #include "random/uniform_int.hpp" #include "random/normal_distribution.hpp" @@ -79,7 +80,7 @@ void run_main() } start = std::chrono::high_resolution_clock::now(); std::cerr << (long)std::chrono::duration_cast(start - stop).count(); - for (int i = 0; i < max_actual_draws; i++) { + for (int i = 0; i < max_actual_draws; i++) { std::cout << hmc.x.getCoefficients().transpose() << std::endl; hmc.apply(rng, 3); samples.col(i) = hmc.x.getCoefficients(); diff --git a/examples/autopoint/userDefinedFunction_autopoint.cpp b/examples/autopoint/userDefinedFunction_autopoint.cpp index 9762bd751..f0f7a734a 100755 --- a/examples/autopoint/userDefinedFunction_autopoint.cpp +++ b/examples/autopoint/userDefinedFunction_autopoint.cpp @@ -22,6 +22,7 @@ #include "Eigen/Eigen" #include "ode_solvers/ode_solvers.hpp" +#include "ode_solvers/oracle_autodiff_functors.hpp" #include "random.hpp" #include "random/uniform_int.hpp" #include "random/normal_distribution.hpp" @@ -77,7 +78,7 @@ void run_main() hmc.apply(rng, 3); } start = std::chrono::high_resolution_clock::now(); - for (int i = 0; i < max_actual_draws; i++) { + for (int i = 0; i < max_actual_draws; i++) { std::cout << hmc.x.getCoefficients().transpose() << std::endl; hmc.apply(rng, 3); samples.col(i) = hmc.x.getCoefficients(); diff --git a/examples/autopoint/userDefinedFunction_manual_gradient.cpp b/examples/autopoint/userDefinedFunction_manual_gradient.cpp index d4d40b10d..5224b3759 100644 --- a/examples/autopoint/userDefinedFunction_manual_gradient.cpp +++ b/examples/autopoint/userDefinedFunction_manual_gradient.cpp @@ -21,6 +21,7 @@ #include "Eigen/Eigen" #include "ode_solvers/ode_solvers.hpp" +#include "ode_solvers/oracle_autodiff_functors.hpp" #include "random.hpp" #include "random/uniform_int.hpp" #include "random/normal_distribution.hpp" @@ -130,7 +131,7 @@ void run_main() { hmc.apply(rng, 3); } start = std::chrono::high_resolution_clock::now(); - for (int i = 0; i < max_actual_draws; i++) { + for (int i = 0; i < max_actual_draws; i++) { std::cout << hmc.x.getCoefficients().transpose() << std::endl; hmc.apply(rng, 3); samples.col(i) = hmc.x.getCoefficients(); diff --git a/examples/correlation_matrices/CMakeLists.txt b/examples/correlation_matrices/CMakeLists.txt index 7871ba5fa..bfc4b513c 100644 --- a/examples/correlation_matrices/CMakeLists.txt +++ b/examples/correlation_matrices/CMakeLists.txt @@ -62,6 +62,16 @@ else() endif(DISABLE_NLP_ORACLES) +option(BUILTIN_AUTODIFF "Use autodiff from ../external" OFF) +include("../../external/cmake-files/Autodiff.cmake") +GetAutodiff() +if (BUILTIN_AUTODIFF) + include_directories (BEFORE ../../external/_deps/Autodiff) +else () + include_directories(BEFORE /usr/local/include) +endif(BUILTIN_AUTODIFF) +add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") #enable the c++17 support needed by autodiff + include("../../external/cmake-files/Eigen.cmake") GetEigen() @@ -125,7 +135,6 @@ else () add_compile_definitions("EIGEN_NO_DEBUG") endif () - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") diff --git a/examples/count-linear-extensions-using-hpolytope/CMakeLists.txt b/examples/count-linear-extensions-using-hpolytope/CMakeLists.txt index d42d2b7a8..f3e66b634 100644 --- a/examples/count-linear-extensions-using-hpolytope/CMakeLists.txt +++ b/examples/count-linear-extensions-using-hpolytope/CMakeLists.txt @@ -98,8 +98,6 @@ else () add_compile_definitions("EIGEN_NO_DEBUG") endif () - - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") diff --git a/include/cartesian_geom/autopoint.h b/include/cartesian_geom/autopoint.h index e1d73f222..41dccf47e 100644 --- a/include/cartesian_geom/autopoint.h +++ b/include/cartesian_geom/autopoint.h @@ -1,8 +1,18 @@ -#ifndef AB9D4FD9_C418_44AC_8276_CC42540EF027 -#define AB9D4FD9_C418_44AC_8276_CC42540EF027 +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2024 Vissarion Fisikopoulos + +// Licensed under GNU LGPL.3, see LICENCE file + +#ifndef CARTESIAN_KERNEL_AUTOPOINT_H +#define CARTESIAN_KERNEL_AUTOPOINT_H + #include #include +/// This class manipulates a point used for automatic differentation +/// parameterized by a number type e.g. double +/// \tparam T Numerical Type template class autopoint { @@ -133,7 +143,7 @@ class autopoint temp.coeffs=coeffs.array().log().matrix(); return temp; } - + autopoint exp() const { autopoint temp; temp.d = d; @@ -217,7 +227,7 @@ class autopoint { return autopoint((Coeff)(coeffs * ((FT)k))); } - + autopoint operator* (T k) { return autopoint((Coeff)(coeffs * ((FT)k))); @@ -225,12 +235,12 @@ class autopoint // matrix multiplication autopoint operator* (const autopoint& autopoint_) { - return autopoint((Coeff)(coeffs * autopoint_.getCoefficients())); + return autopoint((Coeff)(coeffs * autopoint_.getCoefficients())); } // matrix multiplication with normal matrix autopoint operator* (const coeff& matrix_) { - return autopoint(( autopoint(matrix_) * autopoint(coeffs) )); + return autopoint(( autopoint(matrix_) * autopoint(coeffs) )); } void operator*= (const FT k) @@ -283,7 +293,7 @@ class autopoint std::cout<<"\n"; } - static autopoint all_ones(int dim) + static autopoint all_ones(int dim) { autopoint p(dim); for (int i = 0; i < dim; i++) p.set_coord(i, 1.0); @@ -298,7 +308,7 @@ autopoint operator* ( K k, autopoint const& p) return p * k; } -// matrix times autopoint +// matrix times autopoint template autopoint operator* ( Eigen::Matrix matrix_, autopoint const& p) { @@ -308,4 +318,4 @@ autopoint operator* ( Eigen::Matrix matrix -#endif /* AB9D4FD9_C418_44AC_8276_CC42540EF027 */ +#endif // CARTESIAN_KERNEL_AUTOPOINT_H diff --git a/include/ode_solvers/oracle_autodiff_functors.hpp b/include/ode_solvers/oracle_autodiff_functors.hpp new file mode 100644 index 000000000..20500141f --- /dev/null +++ b/include/ode_solvers/oracle_autodiff_functors.hpp @@ -0,0 +1,94 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2012-2024 Vissarion Fisikopoulos +// Copyright (c) 2018-2020 Apostolos Chalkis + +// Licensed under GNU LGPL.3, see LICENCE file + +#ifndef ODE_SOLVERS_ORACLE_AUTODIFF_FUNCTORS_HPP +#define ODE_SOLVERS_ORACLE_AUTODIFF_FUNCTORS_HPP + +#include "Eigen/Eigen" +#include +#include +#include "cartesian_geom/cartesian_kernel.h" +#include "cartesian_geom/autopoint.h" + +struct AutoDiffFunctor { + + template + struct parameters { + unsigned int order; + NT L; // Lipschitz constant for gradient + NT m; // Strong convexity constant + NT kappa; // Condition number + Eigen::Matrix data; + parameters() : order(2), L(4), m(4), kappa(1){}; + }; + + template + struct FunctionFunctor_internal { + using Autopoint = autopoint; + using Coeff = typename autopoint::Coeff; + using FT = typename autopoint::FT; + using Point = typename Cartesian::Point; + + static std::function &)> pdf; + + FT static result_internal(const Coeff &x, const Eigen::Matrix &data){ + return pdf(x, data); // + } + + // external interface + Point static differentiate(Point const &x0, const Eigen::Matrix &data) { + Autopoint x = Autopoint(x0.getCoefficients()); // cast into autopoint + auto x1 = x.getCoefficients(); + Coeff y = autodiff::gradient(result_internal, autodiff::wrt(x1), autodiff::at(x1, data)); + auto result = y.template cast(); + return -1 * Point(result); + } + + NT static result(Point const &x0, const Eigen::Matrix &data) { + Autopoint x = Autopoint(x0.getCoefficients()); // cast to autopoint + auto x1 = x.getCoefficients(); + return result_internal(x1, data).val(); + } + }; + + template + struct GradientFunctor { + using NT = typename Point::FT; + + FunctionFunctor_internal F; + parameters ¶ms; + + GradientFunctor(parameters ¶ms_) : params(params_){}; + + // The index i represents the state vector index + Point operator()(unsigned int const &i, std::vector const &xs, NT const &t) const { + // std::cout<<"calling gradient functor"< + struct FunctionFunctor { + using NT = typename Point::FT; + parameters ¶ms; + + FunctionFunctor(parameters ¶ms_) : params(params_){}; + // The index i represents the state vector index + FunctionFunctor_internal F; + + NT operator()(Point const &x) const { + return F.result(x, params.data); + } + }; +}; + +#endif //ODE_SOLVERS_ORACLE_AUTODIFF_FUNCTORS_HPP \ No newline at end of file diff --git a/include/ode_solvers/oracle_functors.hpp b/include/ode_solvers/oracle_functors.hpp index 80da6d425..35fc8887b 100644 --- a/include/ode_solvers/oracle_functors.hpp +++ b/include/ode_solvers/oracle_functors.hpp @@ -10,12 +10,6 @@ #ifndef ODE_SOLVERS_ORACLE_FUNCTORS_HPP #define ODE_SOLVERS_ORACLE_FUNCTORS_HPP -#include "Eigen/Eigen" -#include -#include -#include "cartesian_geom/cartesian_kernel.h" -#include "cartesian_geom/autopoint.h" - struct OptimizationFunctor { template < @@ -400,84 +394,4 @@ struct HessianFunctor { }; -struct AutoDiffFunctor { -template < - typename NT> -struct parameters { - unsigned int order; - NT L; - // Lipschitz constant for gradient - NT m; - // Strong convexity constant - NT kappa; // Condition number - Eigen::Matrix data; - parameters() : order(2), L(4), m(4), kappa(1){}; -}; - -template < - typename NT> -struct FunctionFunctor_internal { - using Autopoint =autopoint; - using Coeff=typename autopoint::Coeff; - using FT =typename autopoint::FT; - using Kernel=Cartesian; - using Point=typename Kernel::Point; - static std::function &)> pdf; - // static std::function f; - FT static result_internal(const Coeff &x, const Eigen::Matrix &data){ - return pdf(x, data); // - } - // external interface - Point static differentiate(Point const &x0, const Eigen::Matrix &data) { - Autopoint x = Autopoint(x0.getCoefficients()); // cast into autopoint - auto x1 = x.getCoefficients(); - Coeff y = autodiff::gradient(result_internal, autodiff::wrt(x1), autodiff::at(x1, data)); - auto result = y.template cast(); - return -1 * Point(result); - } - - NT static result(Point const &x0, const Eigen::Matrix &data) { - Autopoint x = Autopoint(x0.getCoefficients()); // cast to autopoint - auto x1 = x.getCoefficients(); - return result_internal(x1, data).val(); - } -}; - -template < - typename Point> -struct GradientFunctor { - typedef typename Point::FT NT; - typedef std::vector pts; - typedef FunctionFunctor_internal NegativeLogprobFunctor; - NegativeLogprobFunctor F; - parameters ¶ms; - GradientFunctor(parameters ¶ms_) : params(params_){}; - // The index i represents the state vector index - Point operator()(unsigned int const &i, pts const &xs, NT const &t) const { - // std::cout<<"calling gradient functor"< -struct FunctionFunctor { - typedef typename Point::FT NT; - parameters ¶ms; - FunctionFunctor(parameters ¶ms_) : params(params_){}; - // The index i represents the state vector index - typedef FunctionFunctor_internal NegativeLogprobFunctor; - NegativeLogprobFunctor F; - NT operator()(Point const &x) const { - NT temp = F.result(x, params.data); - // std::cout<const & idx, const Type c){ } } template -void saxpy(MatrixType &a,MatrixType const &b,MatrixType const& c, std::vectorconst & a_idx, std::vectorconst & b_idx){ +void volesti_saxpy(MatrixType &a,MatrixType const &b,MatrixType const& c, std::vectorconst & a_idx, std::vectorconst & b_idx){ for(int i=0;i class two_sided_barrier { VT c = (ub + lb) / 2; VT bias1=VT::Ones(x2, 1) * unbounded_center_coord; - saxpy(c,lb,bias1,lowerIdx,lowerIdx); + volesti_saxpy(c,lb,bias1,lowerIdx,lowerIdx); VT bias2=-VT::Ones(x1, 1) * unbounded_center_coord; - saxpy(c,ub,bias2,upperIdx,upperIdx); + volesti_saxpy(c,ub,bias2,upperIdx,upperIdx); set(c, freeIdx, 0.0); center = c; diff --git a/include/preprocess/crhmc/weighted_two_sided_barrier.h b/include/preprocess/crhmc/weighted_two_sided_barrier.h index 09f5eb798..4df8cc967 100644 --- a/include/preprocess/crhmc/weighted_two_sided_barrier.h +++ b/include/preprocess/crhmc/weighted_two_sided_barrier.h @@ -143,9 +143,9 @@ template class weighted_two_sided_barrier { VT c = (ub + lb) / 2; VT bias1=VT::Ones(x2, 1) * unbounded_center_coord; - saxpy(c,lb,bias1,lowerIdx,lowerIdx); + volesti_saxpy(c,lb,bias1,lowerIdx,lowerIdx); VT bias2=-VT::Ones(x1, 1) * unbounded_center_coord; - saxpy(c,ub,bias2,upperIdx,upperIdx); + volesti_saxpy(c,ub,bias2,upperIdx,upperIdx); set(c, freeIdx, 0.0); center = c; diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index 4482d3b9d..3bde869d2 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -191,6 +191,7 @@ add_definitions(${CMAKE_CXX_FLAGS} "-g") # enable debuger #add_definitions(${CMAKE_CXX_FLAGS} "-Wall") add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler +add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") #enable the c++17 support needed by autodiff #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-ldl") From acad38a684bbd7a152205d29b8328cc2652aa97f Mon Sep 17 00:00:00 2001 From: Elias Tsigaridas Date: Wed, 6 Mar 2024 12:05:59 +0100 Subject: [PATCH 16/35] Refactor/eliminate include directives (#244) * correcting the includes to point the boost library directory * modifs to include only the include/ and external/ directories * modifs * Update CMakeLists.txt --------- Co-authored-by: Vissarion Fisikopoulos Co-authored-by: vfisikop <160006479+vfisikop@users.noreply.github.com> --- include/integration/simple_MC_integration.hpp | 7 ++++--- .../min_sampling_covering_ellipsoid_rounding.hpp | 2 +- test/CMakeLists.txt | 15 --------------- test/benchmarks_cb.cpp | 2 +- test/benchmarks_cg.cpp | 2 +- test/benchmarks_sob.cpp | 2 +- test/boundary_oracles_test.cpp | 9 +++++---- test/logconcave_sampling_test.cpp | 12 +++++++----- test/max_ellipsoid_rounding_test.cpp | 14 ++++++++------ test/mcmc_diagnostics_test.cpp | 14 ++++++++------ test/new_rounding_test.cpp | 13 +++++++------ test/new_volume_example.cpp | 12 ++++++++---- test/ode_solvers_test.cpp | 14 ++++++++------ test/order_polytope.cpp | 15 +++++++++++---- test/sampling_test.cpp | 15 +++++++++------ test/simple_mc_integration.cpp | 12 +++++++----- test/volume_cb_hpolytope.cpp | 14 ++++++++------ test/volume_cb_vpoly_intersection_vpoly.cpp | 16 +++++++++------- test/volume_cb_vpolytope.cpp | 15 +++++++++------ test/volume_cb_zonotopes.cpp | 15 ++++++++------- test/volume_cg_hpolytope.cpp | 14 ++++++++------ test/volume_cg_vpolytope.cpp | 14 ++++++++------ test/volume_sob_hpolytope.cpp | 14 ++++++++------ test/volume_sob_vpolytope.cpp | 14 ++++++++------ 24 files changed, 152 insertions(+), 124 deletions(-) diff --git a/include/integration/simple_MC_integration.hpp b/include/integration/simple_MC_integration.hpp index 58739650d..75f60a1c0 100644 --- a/include/integration/simple_MC_integration.hpp +++ b/include/integration/simple_MC_integration.hpp @@ -16,16 +16,17 @@ #include #include #include -#include "convex_bodies/hpolytope.h" + #include "Eigen/Eigen" +#include "convex_bodies/hpolytope.h" #include "generators/known_polytope_generators.h" -#include "boost_random_number_generator.hpp" +#include "generators/boost_random_number_generator.hpp" #include "cartesian_geom/cartesian_kernel.h" #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" -#include "misc.h" +#include "misc/misc.h" typedef double NT; typedef Cartesian Kernel; diff --git a/include/preprocess/min_sampling_covering_ellipsoid_rounding.hpp b/include/preprocess/min_sampling_covering_ellipsoid_rounding.hpp index 1e97835d9..0a2aba7c5 100644 --- a/include/preprocess/min_sampling_covering_ellipsoid_rounding.hpp +++ b/include/preprocess/min_sampling_covering_ellipsoid_rounding.hpp @@ -11,7 +11,7 @@ #define MIN_ELLIPSOID_ROUNDING_HPP #include -#include "khach.h" +#include "minimum_ellipsoid/khach.h" #include "sampling/random_point_generators.hpp" #include "volume/sampling_policies.hpp" diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index 3bde869d2..a3f8f03d1 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -153,7 +153,6 @@ if (USE_MKL) find_library(GFORTRAN NAME libgfortran.dylib PATHS /usr/local/Cellar/gcc/10.2.0_4/lib/gcc/10) find_library(LAPACK NAME liblapack.dylib PATHS /usr/lib) find_library(OPENMP NAME libiomp5.dylib PATHS /opt/intel/oneapi/compiler/2021.1.1/mac/compiler/lib) - include_directories (BEFORE ${MKLROOT}/include) set(PROJECT_LIBS ${BLAS_LIBRARIES} ${LAPACK_LIBRARIES} ${GFORTRAN_LIBRARIES}) set(MKL_LINK "-L${MKLROOT}/lib -Wl,-rpath,${MKLROOT}/lib -lmkl_intel_ilp64 -lmkl_sequential -lmkl_core -lpthread -lm -ldl") @@ -163,21 +162,7 @@ else() endif(USE_MKL) include_directories (BEFORE ../external) -include_directories (BEFORE ../external/minimum_ellipsoid) -include_directories (BEFORE ../include/generators) -include_directories (BEFORE ../include/volume) include_directories (BEFORE ../include) -include_directories (BEFORE ../include/integration) -include_directories (BEFORE ../include/convex_bodies) -include_directories (BEFORE ../include/convex_bodies/spectrahedra) -include_directories (BEFORE ../include/annealing) -include_directories (BEFORE ../include/ode_solvers) -include_directories (BEFORE ../include/root_finders) -include_directories (BEFORE ../include/samplers) -include_directories (BEFORE ../include/lp_oracles) -include_directories (BEFORE ../include/nlp_oracles) -include_directories (BEFORE ../include/misc) -include_directories (BEFORE ../test) #for Eigen if (${CMAKE_VERSION} VERSION_LESS "3.12.0") diff --git a/test/benchmarks_cb.cpp b/test/benchmarks_cb.cpp index 5837109e3..6d6c95a6d 100644 --- a/test/benchmarks_cb.cpp +++ b/test/benchmarks_cb.cpp @@ -21,7 +21,7 @@ #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" -#include "exact_vols.h" +#include "volume/exact_vols.h" #include "generators/known_polytope_generators.h" int main() diff --git a/test/benchmarks_cg.cpp b/test/benchmarks_cg.cpp index d9c6964d9..4a6a91198 100644 --- a/test/benchmarks_cg.cpp +++ b/test/benchmarks_cg.cpp @@ -21,7 +21,7 @@ #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" -#include "exact_vols.h" +#include "volume/exact_vols.h" #include "generators/known_polytope_generators.h" int main() diff --git a/test/benchmarks_sob.cpp b/test/benchmarks_sob.cpp index 5fe46e209..44350a1c2 100644 --- a/test/benchmarks_sob.cpp +++ b/test/benchmarks_sob.cpp @@ -21,7 +21,7 @@ #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" -#include "exact_vols.h" +#include "volume/exact_vols.h" #include "generators/known_polytope_generators.h" int main() diff --git a/test/boundary_oracles_test.cpp b/test/boundary_oracles_test.cpp index 62cbdf86e..b0a043602 100755 --- a/test/boundary_oracles_test.cpp +++ b/test/boundary_oracles_test.cpp @@ -22,10 +22,11 @@ #include "Eigen/Eigen" #include "doctest.h" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" +#include +#include +#include +#include + #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" #include "volume/volume_cooling_gaussians.hpp" diff --git a/test/logconcave_sampling_test.cpp b/test/logconcave_sampling_test.cpp index 1f67b2851..f0f7fab97 100644 --- a/test/logconcave_sampling_test.cpp +++ b/test/logconcave_sampling_test.cpp @@ -23,13 +23,15 @@ #include "doctest.h" #include "Eigen/Eigen" -#include "ode_solvers.hpp" + +#include +#include +#include +#include + +#include "ode_solvers/ode_solvers.hpp" #include "diagnostics/diagnostics.hpp" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" #include "volume/volume_cooling_gaussians.hpp" diff --git a/test/max_ellipsoid_rounding_test.cpp b/test/max_ellipsoid_rounding_test.cpp index 086dcabe5..a9c25b8c1 100644 --- a/test/max_ellipsoid_rounding_test.cpp +++ b/test/max_ellipsoid_rounding_test.cpp @@ -8,11 +8,13 @@ #include "doctest.h" #include #include -#include "misc.h" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" + +#include +#include +#include +#include + +#include "misc/misc.h" #include "random_walks/random_walks.hpp" @@ -22,7 +24,7 @@ #include "preprocess/max_inscribed_ellipsoid_rounding.hpp" -#include "known_polytope_generators.h" +#include "generators/known_polytope_generators.h" template NT factorial(NT n) diff --git a/test/mcmc_diagnostics_test.cpp b/test/mcmc_diagnostics_test.cpp index 4d303004b..93f4cbbb6 100644 --- a/test/mcmc_diagnostics_test.cpp +++ b/test/mcmc_diagnostics_test.cpp @@ -10,18 +10,20 @@ #include "doctest.h" #include #include -#include "misc.h" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" + +#include +#include +#include +#include + +#include "misc/misc.h" #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" -#include "known_polytope_generators.h" +#include "generators/known_polytope_generators.h" #include "sampling/sampling.hpp" #include "diagnostics/effective_sample_size.hpp" diff --git a/test/new_rounding_test.cpp b/test/new_rounding_test.cpp index b3f6a2adf..87c63a32a 100644 --- a/test/new_rounding_test.cpp +++ b/test/new_rounding_test.cpp @@ -8,12 +8,13 @@ #include "doctest.h" #include #include -#include "misc.h" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" +#include +#include +#include +#include + +#include "misc/misc.h" #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" @@ -24,7 +25,7 @@ #include "preprocess/max_inscribed_ellipsoid_rounding.hpp" #include "preprocess/svd_rounding.hpp" -#include "known_polytope_generators.h" +#include "generators/known_polytope_generators.h" template NT factorial(NT n) diff --git a/test/new_volume_example.cpp b/test/new_volume_example.cpp index 205316559..b2838f04e 100644 --- a/test/new_volume_example.cpp +++ b/test/new_volume_example.cpp @@ -10,10 +10,14 @@ #include "Eigen/Eigen" //#define VOLESTI_DEBUG #include + + #include -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" +#include +#include +#include + + #include "volume/volume_sequence_of_balls.hpp" #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" @@ -21,7 +25,7 @@ #include "misc/misc.h" #include "misc/linear_extensions.h" #include "sampling/sampling.hpp" -#include "exact_vols.h" +#include "volume/exact_vols.h" #include "generators/known_polytope_generators.h" #include "generators/z_polytopes_generators.h" diff --git a/test/ode_solvers_test.cpp b/test/ode_solvers_test.cpp index 735afdef0..4a82472d8 100644 --- a/test/ode_solvers_test.cpp +++ b/test/ode_solvers_test.cpp @@ -18,17 +18,19 @@ #include #include -#include "Eigen/Eigen" #include "doctest.h" +#include "Eigen/Eigen" + +#include +#include +#include +#include + #include "generators/known_polytope_generators.h" -#include "ode_solvers.hpp" +#include "ode_solvers/ode_solvers.hpp" #include "preprocess/crhmc/crhmc_input.h" #include "preprocess/crhmc/crhmc_problem.h" -#include "random.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_int.hpp" -#include "random/uniform_real_distribution.hpp" #include "random_walks/random_walks.hpp" #include "volume/volume_cooling_balls.hpp" #include "volume/volume_cooling_gaussians.hpp" diff --git a/test/order_polytope.cpp b/test/order_polytope.cpp index ddc7b54f3..e2ad4300d 100644 --- a/test/order_polytope.cpp +++ b/test/order_polytope.cpp @@ -11,12 +11,19 @@ #include "doctest.h" #include #include + + +#include +#include +#include +#include + #include "cartesian_geom/cartesian_kernel.h" #include "cartesian_geom/point.h" -#include "poset.h" -#include "orderpolytope.h" -#include "misc.h" -#include "random.hpp" +#include "convex_bodies/orderpolytope.h" +#include "misc/poset.h" +#include "misc/misc.h" + template diff --git a/test/sampling_test.cpp b/test/sampling_test.cpp index 3831e1ea4..8e82e5818 100644 --- a/test/sampling_test.cpp +++ b/test/sampling_test.cpp @@ -11,16 +11,19 @@ #include "doctest.h" #include #include -#include "misc.h" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" + + +#include +#include +#include +#include + +#include "misc/misc.h" #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" -#include "known_polytope_generators.h" +#include "generators/known_polytope_generators.h" #include "sampling/sampling.hpp" #include "diagnostics/univariate_psrf.hpp" diff --git a/test/simple_mc_integration.cpp b/test/simple_mc_integration.cpp index ca633f8f6..c1b6b9ecd 100644 --- a/test/simple_mc_integration.cpp +++ b/test/simple_mc_integration.cpp @@ -10,18 +10,20 @@ // Link to the Latte-Integrale Software: https://www.math.ucdavis.edu/~latte/software.php // Link to the tests: https://github.com/surajchoubey/latte-integrale-checks +#include +#include +#include + #include "doctest.h" -#include "simple_MC_integration.hpp" + #include "Eigen/Eigen" -#include +#include "integration/simple_MC_integration.hpp" #include "cartesian_geom/cartesian_kernel.h" #include "convex_bodies/hpolytope.h" #include "generators/known_polytope_generators.h" #include "ode_solvers/oracle_functors.hpp" #include "random_walks/random_walks.hpp" -#include -#include -#include "misc.h" +#include "misc/misc.h" template NT exp_normsq(Point X) { diff --git a/test/volume_cb_hpolytope.cpp b/test/volume_cb_hpolytope.cpp index cd0df2d83..e938cc255 100644 --- a/test/volume_cb_hpolytope.cpp +++ b/test/volume_cb_hpolytope.cpp @@ -10,18 +10,20 @@ #include "doctest.h" #include #include -#include "misc.h" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" + +#include +#include +#include +#include + +#include "misc/misc.h" #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" -#include "known_polytope_generators.h" +#include "generators/known_polytope_generators.h" template NT factorial(NT n) diff --git a/test/volume_cb_vpoly_intersection_vpoly.cpp b/test/volume_cb_vpoly_intersection_vpoly.cpp index a9f7d9cb6..204a560bf 100644 --- a/test/volume_cb_vpoly_intersection_vpoly.cpp +++ b/test/volume_cb_vpoly_intersection_vpoly.cpp @@ -8,20 +8,22 @@ #include "doctest.h" #include #include -#include "misc.h" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" + +#include +#include +#include +#include + +#include "misc/misc.h" #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" -#include "known_polytope_generators.h" -#include "v_polytopes_generators.h" +#include "generators/known_polytope_generators.h" +#include "generators/v_polytopes_generators.h" template NT factorial(NT n) diff --git a/test/volume_cb_vpolytope.cpp b/test/volume_cb_vpolytope.cpp index eb32dd4dc..7b261ac18 100644 --- a/test/volume_cb_vpolytope.cpp +++ b/test/volume_cb_vpolytope.cpp @@ -8,18 +8,21 @@ #include "doctest.h" #include #include -#include "misc.h" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" + + +#include +#include +#include +#include + +#include "misc/misc.h" #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" -#include "known_polytope_generators.h" +#include "generators/known_polytope_generators.h" template NT factorial(NT n) diff --git a/test/volume_cb_zonotopes.cpp b/test/volume_cb_zonotopes.cpp index 77bc6db20..a6b40d7d3 100644 --- a/test/volume_cb_zonotopes.cpp +++ b/test/volume_cb_zonotopes.cpp @@ -8,12 +8,13 @@ #include "doctest.h" #include #include -#include "misc.h" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" +#include +#include +#include +#include + +#include "misc/misc.h" #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" @@ -21,8 +22,8 @@ #include "volume/volume_cooling_balls.hpp" #include "volume/volume_cooling_hpoly.hpp" -#include "exact_vols.h" -#include "z_polytopes_generators.h" +#include "volume/exact_vols.h" +#include "generators/z_polytopes_generators.h" template NT factorial(NT n) diff --git a/test/volume_cg_hpolytope.cpp b/test/volume_cg_hpolytope.cpp index 2d2f5bb97..2e9060e45 100644 --- a/test/volume_cg_hpolytope.cpp +++ b/test/volume_cg_hpolytope.cpp @@ -8,18 +8,20 @@ #include "doctest.h" #include #include -#include "misc.h" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" + +#include +#include +#include +#include + +#include "misc/misc.h" #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" -#include "known_polytope_generators.h" +#include "generators/known_polytope_generators.h" template NT factorial(NT n) diff --git a/test/volume_cg_vpolytope.cpp b/test/volume_cg_vpolytope.cpp index 7809199ff..eac97d976 100644 --- a/test/volume_cg_vpolytope.cpp +++ b/test/volume_cg_vpolytope.cpp @@ -8,18 +8,20 @@ #include "doctest.h" #include #include -#include "misc.h" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" + +#include +#include +#include +#include + +#include "misc/misc.h" #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" -#include "known_polytope_generators.h" +#include "generators/known_polytope_generators.h" template NT factorial(NT n) diff --git a/test/volume_sob_hpolytope.cpp b/test/volume_sob_hpolytope.cpp index 18fa28bb0..0d2ea5c85 100644 --- a/test/volume_sob_hpolytope.cpp +++ b/test/volume_sob_hpolytope.cpp @@ -8,18 +8,20 @@ #include "doctest.h" #include #include -#include "misc.h" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" + +#include +#include +#include +#include + +#include "misc/misc.h" #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" -#include "known_polytope_generators.h" +#include "generators/known_polytope_generators.h" template NT factorial(NT n) diff --git a/test/volume_sob_vpolytope.cpp b/test/volume_sob_vpolytope.cpp index 41b963be2..76b3c784a 100644 --- a/test/volume_sob_vpolytope.cpp +++ b/test/volume_sob_vpolytope.cpp @@ -8,18 +8,20 @@ #include "doctest.h" #include #include -#include "misc.h" -#include "random.hpp" -#include "random/uniform_int.hpp" -#include "random/normal_distribution.hpp" -#include "random/uniform_real_distribution.hpp" + +#include +#include +#include +#include + +#include "misc/misc.h" #include "random_walks/random_walks.hpp" #include "volume/volume_sequence_of_balls.hpp" #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" -#include "known_polytope_generators.h" +#include "generators/known_polytope_generators.h" template NT factorial(NT n) From 8f7a9f25c2af3b3722644c9d3de137e4cb9fce00 Mon Sep 17 00:00:00 2001 From: vfisikop Date: Wed, 6 Mar 2024 12:17:45 +0200 Subject: [PATCH 17/35] Remove R interface and update CI to test Rvolesti repository with current develop --- .github/workflows/R-CMD-check-macOS.yml | 11 +- .github/workflows/R-CMD-check-ubuntu.yml | 11 +- .github/workflows/R-CMD-check-windows.yml | 11 +- R-proj/DESCRIPTION | 23 - R-proj/NAMESPACE | 42 - 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include/ode_solvers/oracle_functors_rcpp.hpp diff --git a/.github/workflows/R-CMD-check-macOS.yml b/.github/workflows/R-CMD-check-macOS.yml index 23ac6d616..b1ac281c3 100644 --- a/.github/workflows/R-CMD-check-macOS.yml +++ b/.github/workflows/R-CMD-check-macOS.yml @@ -41,12 +41,17 @@ jobs: - name: Install dependencies run: Rscript -e "install.packages(c('devtools', dependencies=TRUE))" -e "install.packages(c('rcmdcheck', 'devtools', 'Rcpp', 'RcppEigen', 'BH', 'testthat', 'downloader', 'xfun'))"; + - name: Checkout Rvolesti repository + run: git clone https://github.com/GeomScale/Rvolesti.git; + + - name: Upgrade with current develop of volesti + run: cp -rf include Rvolesti/src/volesti; + cp -rf external Rvolesti/src; + - name: Check env: _R_CHECK_CRAN_INCOMING_REMOTE_: false - run: cd cran_gen/; - Rscript genCRANpkg.R; - cd cran_package; + run: cd Rvolesti/; Rscript -e "library(rcmdcheck)" -e "rcmdcheck::rcmdcheck(args = c('--no-manual'), error_on = 'warning', check_dir = 'check')" - name: Upload check results diff --git a/.github/workflows/R-CMD-check-ubuntu.yml b/.github/workflows/R-CMD-check-ubuntu.yml index 9fe81de99..753ee114a 100644 --- a/.github/workflows/R-CMD-check-ubuntu.yml +++ b/.github/workflows/R-CMD-check-ubuntu.yml @@ -44,12 +44,17 @@ jobs: - name: Install dependencies run: Rscript -e "install.packages(c('testthat', 'pkgload', 'rcmdcheck', 'devtools', 'Rcpp', 'RcppEigen', 'BH', 'downloader', 'xfun', dependencies=TRUE))"; + - name: Checkout Rvolesti repository + run: git clone https://github.com/GeomScale/Rvolesti.git; + + - name: Upgrade with current develop of volesti + run: cp -rf include Rvolesti/src/volesti; + cp -rf external Rvolesti/src; + - name: Check env: _R_CHECK_CRAN_INCOMING_REMOTE_: false - run: cd cran_gen/; - Rscript genCRANpkg.R; - cd cran_package; + run: cd Rvolesti/; Rscript -e "library(rcmdcheck)" -e "rcmdcheck::rcmdcheck(args = c('--no-manual'), error_on = 'warning', check_dir = 'check')" - name: Upload check results diff --git a/.github/workflows/R-CMD-check-windows.yml b/.github/workflows/R-CMD-check-windows.yml index ad56a7170..e5c1cf7b3 100644 --- a/.github/workflows/R-CMD-check-windows.yml +++ b/.github/workflows/R-CMD-check-windows.yml @@ -40,12 +40,17 @@ jobs: - name: Install dependencies run: Rscript -e "install.packages(c('devtools', dependencies=TRUE))" -e "install.packages(c('rcmdcheck', 'devtools', 'Rcpp', 'RcppEigen', 'BH', 'testthat', 'downloader', 'xfun'))" + - name: Checkout Rvolesti repository + run: git clone https://github.com/GeomScale/Rvolesti.git; + + - name: Upgrade with current develop of volesti + run: cp -rf include Rvolesti/src/volesti; + cp -rf external Rvolesti/src; + - name: Check env: _R_CHECK_CRAN_INCOMING_REMOTE_: false - run: cd cran_gen/; - Rscript genCRANpkg.R; - cd cran_package; + run: cd Rvolesti/; Rscript -e "library(rcmdcheck)" -e "rcmdcheck::rcmdcheck(args = c('--no-manual'), error_on = 'warning', check_dir = 'check')" - name: Upload check results diff --git a/R-proj/DESCRIPTION b/R-proj/DESCRIPTION deleted file mode 100644 index ce188ad22..000000000 --- a/R-proj/DESCRIPTION +++ /dev/null @@ -1,23 +0,0 @@ -Package: volesti -Type: Package -License: LGPL-3 -Title: Volume Approximation and Sampling of Convex Polytopes -Author: Vissarion Fisikopoulos [aut, cph, cre], - Apostolos Chalkis [cph, aut], - contributors in file inst/AUTHORS -Copyright: file inst/COPYRIGHTS -Description: Provides an R interface for 'volesti' C++ package. 'volesti' computes estimations of volume - of polytopes given by (i) a set of points, (ii) linear inequalities or (iii) Minkowski sum of segments - (a.k.a. zonotopes). There are three algorithms for volume estimation as well as algorithms - for sampling, rounding and rotating polytopes. Moreover, 'volesti' provides algorithms for - estimating copulas useful in computational finance. -Version: 1.1.2 -Date: 2020-10-24 -Maintainer: Vissarion Fisikopoulos -Depends: Rcpp (>= 0.12.17) -Imports: methods, stats -LinkingTo: Rcpp, RcppEigen, BH -Suggests: testthat -Encoding: UTF-8 -RoxygenNote: 7.1.1 -BugReports: https://github.com/GeomScale/volume_approximation/issues diff --git a/R-proj/NAMESPACE b/R-proj/NAMESPACE deleted file mode 100644 index 085d9ed9f..000000000 --- a/R-proj/NAMESPACE +++ /dev/null @@ -1,42 +0,0 @@ -# Generated by roxygen2: do not edit by hand - -export(compute_indicators) -export(copula) -export(dinvweibull_with_loc) -export(direct_sampling) -export(ess) -export(estimtate_lipschitz_constant) -export(exact_vol) -export(file_to_polytope) -export(frustum_of_simplex) -export(gen_birkhoff) -export(gen_cross) -export(gen_cube) -export(gen_prod_simplex) -export(gen_rand_hpoly) -export(gen_rand_vpoly) -export(gen_rand_zonotope) -export(gen_simplex) -export(gen_skinny_cube) -export(geweke) -export(inner_ball) -export(loadSdpaFormatFile) -export(ode_solve) -export(pinvweibull_with_loc) -export(psrf_multivariate) -export(psrf_univariate) -export(raftery) -export(readSdpaFormatFile) -export(rotate_polytope) -export(round_polytope) -export(sample_points) -export(volume) -export(writeSdpaFormatFile) -export(zonotope_approximation) -exportPattern("^[[:alpha:]]+") -importFrom("methods","new") -importFrom("stats","cov") -importFrom("utils","read.csv") -importFrom(Rcpp,evalCpp) -importFrom(Rcpp,loadModule) -useDynLib(volesti, .registration=TRUE) diff --git a/R-proj/R/RcppExports.R b/R-proj/R/RcppExports.R deleted file mode 100644 index c3f192aee..000000000 --- a/R-proj/R/RcppExports.R +++ /dev/null @@ -1,459 +0,0 @@ -# Generated by using Rcpp::compileAttributes() -> do not edit by hand -# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 - -#' Construct a copula using uniform sampling from the unit simplex -#' -#' Given two families of parallel hyperplanes or a family of parallel hyperplanes and a family of concentric ellispoids centered at the origin intersecting the canonical simplex, this function uniformly samples from the canonical simplex and construct an approximation of the bivariate probability distribution, called copula (see \url{https://en.wikipedia.org/wiki/Copula_(probability_theory)}). -#' At least two families of hyperplanes or one family of hyperplanes and one family of ellipsoids have to be given as input. -#' -#' @param r1 The \eqn{d}-dimensional normal vector of the first family of parallel hyperplanes. -#' @param r2 Optional. The \eqn{d}-dimensional normal vector of the second family of parallel hyperplanes. -#' @param sigma Optional. The \eqn{d\times d} symmetric positive semidefine matrix that describes the family of concentric ellipsoids centered at the origin. -#' @param m The number of the slices for the copula. The default value is 100. -#' @param n The number of points to sample. The default value is \eqn{5\cdot 10^5}. -#' @param seed Optional. A fixed seed for the number generator. -#' -#' @references \cite{L. Cales, A. Chalkis, I.Z. Emiris, V. Fisikopoulos, -#' \dQuote{Practical volume computation of structured convex bodies, and an application to modeling portfolio dependencies and financial crises,} \emph{Proc. of Symposium on Computational Geometry, Budapest, Hungary,} 2018.} -#' -#' @return A \eqn{m\times m} numerical matrix that corresponds to a copula. -#' @examples -#' # compute a copula for two random families of parallel hyperplanes -#' h1 = runif(n = 10, min = 1, max = 1000) -#' h1 = h1 / 1000 -#' h2=runif(n = 10, min = 1, max = 1000) -#' h2 = h2 / 1000 -#' cop = copula(r1 = h1, r2 = h2, m = 10, n = 100000) -#' -#' # compute a copula for a family of parallel hyperplanes and a family of conentric ellipsoids -#' h = runif(n = 10, min = 1, max = 1000) -#' h = h / 1000 -#' E = replicate(10, rnorm(20)) -#' E = cov(E) -#' cop = copula(r1 = h, sigma = E, m = 10, n = 100000) -#' -#' @export -copula <- function(r1, r2 = NULL, sigma = NULL, m = NULL, n = NULL, seed = NULL) { - .Call(`_volesti_copula`, r1, r2, sigma, m, n, seed) -} - -#' Sample perfect uniformly distributed points from well known convex bodies: (a) the unit simplex, (b) the canonical simplex, (c) the boundary of a hypersphere or (d) the interior of a hypersphere. -#' -#' The \eqn{d}-dimensional unit simplex is the set of points \eqn{\vec{x}\in \R^d}, s.t.: \eqn{\sum_i x_i\leq 1}, \eqn{x_i\geq 0}. The \eqn{d}-dimensional canonical simplex is the set of points \eqn{\vec{x}\in \R^d}, s.t.: \eqn{\sum_i x_i = 1}, \eqn{x_i\geq 0}. -#' -#' @param body A list to request exact uniform sampling from special well known convex bodies through the following input parameters: -#' \itemize{ -#' \item{\code{type} }{ A string that declares the type of the body for the exact sampling: a) \code{'unit_simplex'} for the unit simplex, b) \code{'canonical_simplex'} for the canonical simplex, c) \code{'hypersphere'} for the boundary of a hypersphere centered at the origin, d) \code{'ball'} for the interior of a hypersphere centered at the origin.} -#' \item{\code{dimension} }{ An integer that declares the dimension when exact sampling is enabled for a simplex or a hypersphere.} -#' \item{\code{radius} }{ The radius of the \eqn{d}-dimensional hypersphere. The default value is \eqn{1}.} -#' } -#' @param n The number of points that the function is going to sample. -#' @param seed Optional. A fixed seed for the number generator. -#' -#' @references \cite{R.Y. Rubinstein and B. Melamed, -#' \dQuote{Modern simulation and modeling} \emph{ Wiley Series in Probability and Statistics,} 1998.} -#' @references \cite{A Smith, Noah and W Tromble, Roy, -#' \dQuote{Sampling Uniformly from the Unit Simplex,} \emph{ Center for Language and Speech Processing Johns Hopkins University,} 2004.} -#' -#' @return A \eqn{d\times n} matrix that contains, column-wise, the sampled points from the convex polytope P. -#' @examples -#' # 100 uniform points from the 2-d unit ball -#' points = direct_sampling(n = 100, body = list("type" = "ball", "dimension" = 2)) -#' @export -direct_sampling <- function(body, n, seed = NULL) { - .Call(`_volesti_direct_sampling`, body, n, seed) -} - -#' Gelman-Rubin and Brooks-Gelman Potential Scale Reduction Factor (PSRF) for each marginal -#' -#' @param samples A matrix that contans column-wise the sampled points from a geometric random walk. -#' -#' @references \cite{Gelman, A. and Rubin, D. B., -#' \dQuote{Inference from iterative simulation using multiple sequences,} \emph{Statistical Science,} 1992.} -#' -#' @references \cite{Brooks, S. and Gelman, A., -#' \dQuote{General Methods for Monitoring Convergence of Iterative Simulations,} \emph{Journal of Computational and Graphical Statistics,} 1998.} -#' -#' @return A vector that contains the values of PSRF for each coordinate -#' -#' @export -ess <- function(samples) { - .Call(`_volesti_ess`, samples) -} - -#' Compute the exact volume of (a) a zonotope (b) an arbitrary simplex in V-representation or (c) if the volume is known and declared by the input object. -#' -#' Given a zonotope (as an object of class Zonotope), this function computes the sum of the absolute values of the determinants of all the \eqn{d \times d} submatrices of the \eqn{m\times d} matrix \eqn{G} that contains row-wise the \eqn{m} \eqn{d}-dimensional segments that define the zonotope. -#' For an arbitrary simplex that is given in V-representation this function computes the absolute value of the determinant formed by the simplex's points assuming it is shifted to the origin. -#' -#' @param P A polytope -#' -#' @references \cite{E. Gover and N. Krikorian, -#' \dQuote{Determinants and the Volumes of Parallelotopes and Zonotopes,} \emph{Linear Algebra and its Applications, 433(1), 28 - 40,} 2010.} -#' -#' @return The exact volume of the input polytope, for zonotopes, simplices in V-representation and polytopes with known exact volume -#' @examples -#' -#' # compute the exact volume of a 5-dimensional zonotope defined by the Minkowski sum of 10 segments -#' Z = gen_rand_zonotope(2, 5) -#' vol = exact_vol(Z) -#' -#' \donttest{# compute the exact volume of a 2-d arbitrary simplex -#' V = matrix(c(2,3,-1,7,0,0),ncol = 2, nrow = 3, byrow = TRUE) -#' P = Vpolytope$new(V) -#' vol = exact_vol(P) -#' } -#' -#' # compute the exact volume the 10-dimensional cross polytope -#' P = gen_cross(10,'V') -#' vol = exact_vol(P) -#' @export -exact_vol <- function(P) { - .Call(`_volesti_exact_vol`, P) -} - -#' Compute the percentage of the volume of the simplex that is contained in the intersection of a half-space and the simplex. -#' -#' A half-space \eqn{H} is given as a pair of a vector \eqn{a\in R^d} and a scalar \eqn{z0\in R} s.t.: \eqn{a^Tx\leq z0}. This function calls the Ali's version of the Varsi formula to compute a frustum of the simplex. -#' -#' @param a A \eqn{d}-dimensional vector that defines the direction of the hyperplane. -#' @param z0 The scalar that defines the half-space. -#' -#' @references \cite{Varsi, Giulio, -#' \dQuote{The multidimensional content of the frustum of the simplex,} \emph{Pacific J. Math. 46, no. 1, 303--314,} 1973.} -#' -#' @references \cite{Ali, Mir M., -#' \dQuote{Content of the frustum of a simplex,} \emph{ Pacific J. Math. 48, no. 2, 313--322,} 1973.} -#' -#' @return The percentage of the volume of the simplex that is contained in the intersection of a given half-space and the simplex. -#' -#' @examples -#' # compute the frustum of H: -x1+x2<=0 -#' a=c(-1,1) -#' z0=0 -#' frustum = frustum_of_simplex(a, z0) -#' @export -frustum_of_simplex <- function(a, z0) { - .Call(`_volesti_frustum_of_simplex`, a, z0) -} - -#' Geweke's MCMC diagnostic -#' -#' @param samples A matrix that contans column-wise the sampled points from a geometric random walk. -#' @param frac_first Optional. The portion of the first in order points in matrix samples. -#' @param frac_last Optional. The portion of the last in order points in matrix samples. -#' -#' @references \cite{Geweke, J., -#' \dQuote{Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments,} \emph{ In Bayesian Statistics 4. Proceedings of the Fourth Valencia International Meeting,} 1992.} -#' -#' @return A boolean to denote if the result of Geweke diagnostic: (i) false if the null hypothesis is rejected, (ii) true if the null hypothesis is not rejected. -#' -#' @export -geweke <- function(samples, frac_first = NULL, frac_last = NULL) { - .Call(`_volesti_geweke`, samples, frac_first, frac_last) -} - -#' Compute an inscribed ball of a convex polytope -#' -#' For a H-polytope described by a \eqn{m\times d} matrix \eqn{A} and a \eqn{m}-dimensional vector \eqn{b}, s.t.: \eqn{P=\{x\ |\ Ax\leq b\} }, this function computes the largest inscribed ball (Chebychev ball) by solving the corresponding linear program. -#' For both zonotopes and V-polytopes the function computes the minimum \eqn{r} s.t.: \eqn{ r e_i \in P} for all \eqn{i=1, \dots ,d}. Then the ball centered at the origin with radius \eqn{r/ \sqrt{d}} is an inscribed ball. -#' -#' @param P A convex polytope. It is an object from class (a) Hpolytope or (b) Vpolytope or (c) Zonotope or (d) VpolytopeIntersection. -#' @param lpsolve Optional. A boolean variable to compute the Chebychev ball of an H-polytope using the lpsolve library. -#' -#' @return A \eqn{(d+1)}-dimensional vector that describes the inscribed ball. The first \eqn{d} coordinates corresponds to the center of the ball and the last one to the radius. -#' -#' @examples -#' # compute the Chebychev ball of the 2d unit simplex -#' P = gen_cube(10,'H') -#' ball_vec = inner_ball(P) -#' -#' # compute an inscribed ball of the 3-dimensional unit cube in V-representation -#' P = gen_cube(3, 'V') -#' ball_vec = inner_ball(P, lpsolve = TRUE) -#' @export -inner_ball <- function(P, lpsolve = NULL) { - .Call(`_volesti_inner_ball`, P, lpsolve) -} - -#' Solve an ODE of the form dx^n / dt^n = F(x, t) -#' -#' @param n The number of steps. -#' @param step_size The step size. -#' @param order The ODE order (default is n = 1) -#' @param dimension The dimension of each derivative -#' @param initial_time The initial time -#' @param F The function oracle F(x, t) in the ODE. -#' @param method The method to be used -#' @param initial_conditions The initial conditions provided to the solver. Must be provided in a list with keys "x_1", ..., "x_n" and column vectors as values. The state "x_n" represents the (n-1)-th order derivative with respect to time -#' @param domains A list of n H-polytopes with keys "P_1", "P_2", ..., "P_n" that correspond to each derivative's domain -#' -#' @return A list which contains elements "x_1", ..., "x_n" representing each derivative results. Each "x_i" corresponds to a d x n matrix where each column represents a certain timestep of the solver. -#' -#' @examples -#' # Please visit the examples directory on examples demonstrating usage of the ODE solvers. -#' -#' @export -ode_solve <- function(n, step_size, order, dimension, initial_time, F, method, domains = NULL, initial_conditions = NULL) { - .Call(`_volesti_ode_solve`, n, step_size, order, dimension, initial_time, F, method, domains, initial_conditions) -} - -#' An internal Rccp function as a polytope generator -#' -#' @param kind_gen An integer to declare the type of the polytope. -#' @param Vpoly_gen A boolean parameter to declare if the requested polytope has to be in V-representation. -#' @param Zono_gen A boolean parameter to declare if the requested polytope has to be a zonotope. -#' @param dim_gen An integer to declare the dimension of the requested polytope. -#' @param m_gen An integer to declare the number of generators for the requested random zonotope or the number of vertices for a V-polytope. -#' @param seed Optional. A fixed seed for the random polytope generator. -#' -#' @keywords internal -#' -#' @return A numerical matrix describing the requested polytope -poly_gen <- function(kind_gen, Vpoly_gen, Zono_gen, dim_gen, m_gen, seed = NULL) { - .Call(`_volesti_poly_gen`, kind_gen, Vpoly_gen, Zono_gen, dim_gen, m_gen, seed) -} - -#' Gelman-Rubin Potential Scale Reduction Factor (PSRF) -#' -#' @param samples A matrix that contans column-wise the sampled points from a geometric random walk. -#' -#' @references \cite{Gelman, A. and Rubin, D. B., -#' \dQuote{Inference from iterative simulation using multiple sequences,} \emph{Statistical Science,} 1992.} -#' -#' @references \cite{Brooks, S. and Gelman, A., -#' \dQuote{General Methods for Monitoring Convergence of Iterative Simulations,} \emph{Journal of Computational and Graphical Statistics,} 1998.} -#' -#' @return The value of multivariate PSRF by S. Brooks and A. Gelman. -#' -#' @export -psrf_multivariate <- function(samples) { - .Call(`_volesti_psrf_multivariate`, samples) -} - -#' Gelman-Rubin and Brooks-Gelman Potential Scale Reduction Factor (PSRF) for each marginal -#' -#' @param samples A matrix that contans column-wise the sampled points from a geometric random walk. -#' @param method A string to reauest diagnostic: (i) \code{'normal'} for psrf of Gelman-Rubin and (ii) \code{'interval'} for psrf of Brooks-Gelman. -#' -#' @references \cite{Gelman, A. and Rubin, D. B., -#' \dQuote{Inference from iterative simulation using multiple sequences,} \emph{Statistical Science,} 1992.} -#' -#' @references \cite{Brooks, S. and Gelman, A., -#' \dQuote{General Methods for Monitoring Convergence of Iterative Simulations,} \emph{Journal of Computational and Graphical Statistics,} 1998.} -#' -#' @return A vector that contains the values of PSRF for each coordinate -#' -#' @export -psrf_univariate <- function(samples, method = NULL) { - .Call(`_volesti_psrf_univariate`, samples, method) -} - -#' Raftery and Lewis MCMC diagnostic -#' -#' @param samples A matrix that contans column-wise the sampled points from a geometric random walk. -#' @param q Optional. The quantile of the quantity of interest. The default value is 0.025. -#' @param r Optional. The level of precision desired. The default value is 0.01. -#' @param s Optional. The probability associated with r. The default value is 0.95. -#' -#' @references \cite{Raftery, A. E. and Lewis, S. M., -#' \dQuote{How many iterations in the Gibbs sampler?,} \emph{Bayesian Statistics 4. Proceedings of the Fourth Valencia International Meeting,} 1992.} -#' -#' @return (i) The number of draws required for burn-in, (ii) the skip parameter for 1st-order Markov chain, (iii) the skip parameter sufficient to get independence chain, (iv) the number of draws required to achieve r precision, (v) the number of draws if the chain is white noise, (vi) the I-statistic from Raftery and Lewis (1992). -#' -#' @export -raftery <- function(samples, q = NULL, r = NULL, s = NULL) { - .Call(`_volesti_raftery`, samples, q, r, s) -} - -#' An internal Rccp function for the random rotation of a convex polytope -#' -#' @param P A convex polytope (H-, V-polytope or a zonotope). -#' @param T Optional. A rotation matrix. -#' @param seed Optional. A fixed seed for the random linear map generator. -#' -#' @keywords internal -#' -#' @return A matrix that describes the rotated polytope -rotating <- function(P, T = NULL, seed = NULL) { - .Call(`_volesti_rotating`, P, T, seed) -} - -#' Internal rcpp function for the rounding of a convex polytope -#' -#' @param P A convex polytope (H- or V-representation or zonotope). -#' @param method Optional. The method to use for rounding, a) \code{'min_ellipsoid'} for the method based on mimimmum volume enclosing ellipsoid of a uniform sample from P, b) \code{'max_ellipsoid'} for the method based on maximum volume enclosed ellipsoid in P, (c) \code{'isotropy'} for the method based on isotropy. The default method is \code{'min_ellipsoid'} for all the representations. -#' @param seed Optional. A fixed seed for the number generator. -#' -#' @keywords internal -#' -#' @return A numerical matrix that describes the rounded polytope, a numerical matrix of the inverse linear transofmation that is applied on the input polytope, the numerical vector the the input polytope is shifted and the determinant of the matrix of the linear transformation that is applied on the input polytope. -rounding <- function(P, method = NULL, seed = NULL) { - .Call(`_volesti_rounding`, P, method, seed) -} - -#' Sample uniformly, normally distributed, or logconcave distributed points from a convex Polytope (H-polytope, V-polytope, zonotope or intersection of two V-polytopes). -#' -#' @param P A convex polytope. It is an object from class (a) Hpolytope or (b) Vpolytope or (c) Zonotope or (d) VpolytopeIntersection. -#' @param n The number of points that the function is going to sample from the convex polytope. -#' @param random_walk Optional. A list that declares the random walk and some related parameters as follows: -#' \itemize{ -#' \item{\code{walk} }{ A string to declare the random walk: i) \code{'CDHR'} for Coordinate Directions Hit-and-Run, ii) \code{'RDHR'} for Random Directions Hit-and-Run, iii) \code{'BaW'} for Ball Walk, iv) \code{'BiW'} for Billiard walk, v) \code{'dikin'} for dikin walk, vi) \code{'vaidya'} for vaidya walk, vii) \code{'john'} for john walk, viii) \code{'BCDHR'} boundary sampling by keeping the extreme points of CDHR or ix) \code{'BRDHR'} boundary sampling by keeping the extreme points of RDHR x) \code{'HMC'} for Hamiltonian Monte Carlo (logconcave densities) xi) \code{'ULD'} for Underdamped Langevin Dynamics using the Randomized Midpoint Method xii) \code{'ExactHMC'} for exact Hamiltonian Monte Carlo with reflections (spherical Gaussian or exponential distribution). The default walk is \code{'aBiW'} for the uniform distribution or \code{'CDHR'} for the Gaussian distribution and H-polytopes and \code{'BiW'} or \code{'RDHR'} for the same distributions and V-polytopes and zonotopes.} -#' \item{\code{walk_length} }{ The number of the steps per generated point for the random walk. The default value is \eqn{1}.} -#' \item{\code{nburns} }{ The number of points to burn before start sampling. The default value is \eqn{1}.} -#' \item{\code{starting_point} }{ A \eqn{d}-dimensional numerical vector that declares a starting point in the interior of the polytope for the random walk. The default choice is the center of the ball as that one computed by the function \code{inner_ball()}.} -#' \item{\code{BaW_rad} }{ The radius for the ball walk.} -#' \item{\code{L} }{ The maximum length of the billiard trajectory or the radius for the step of dikin, vaidya or john walk.} -#' \item{\code{solver} }{ Specify ODE solver for logconcave sampling. Options are i) leapfrog, ii) euler iii) runge-kutta iv) richardson} -#' \item{\code{step_size }{ Optionally chosen step size for logconcave sampling. Defaults to a theoretical value if not provided.} -#' } -#' @param distribution Optional. A list that declares the target density and some related parameters as follows: -#' \itemize{ -#' \item{\code{density} }{ A string: (a) \code{'uniform'} for the uniform distribution or b) \code{'gaussian'} for the multidimensional spherical distribution c) \code{logconcave} with form proportional to exp(-f(x)) where f(x) is L-smooth and m-strongly-convex d) \code{'exponential'} for the exponential distribution. The default target distribution is the uniform distribution.} -#' \item{\code{variance} }{ The variance of the multidimensional spherical gaussian or the exponential distribution. The default value is 1.} -#' \item{\code{mode} }{ A \eqn{d}-dimensional numerical vector that declares the mode of the Gaussian distribution. The default choice is the center of the as that one computed by the function \code{inner_ball()}.} -#' \item{\code{bias} }{ The bias vector for the exponential distribution. The default vector is \eqn{c_1 = 1} and \eqn{c_i = 0} for \eqn{i \neq 1}.} -#' \item{\code{L_} }{ Smoothness constant (for logconcave). } -#' \item{\code{m} }{ Strong-convexity constant (for logconcave). } -#' \item{\code{negative_logprob} }{ Negative log-probability (for logconcave). } -#' \item{\code{negative_logprob_gradient} }{ Negative log-probability gradient (for logconcave). } -#' } -#' @param seed Optional. A fixed seed for the number generator. -#' -#' @references \cite{Robert L. Smith, -#' \dQuote{Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed Over Bounded Regions,} \emph{Operations Research,} 1984.}, -#' -#' @references \cite{B.T. Polyak, E.N. Gryazina, -#' \dQuote{Billiard walk - a new sampling algorithm for control and optimization,} \emph{IFAC Proceedings Volumes,} 2014.}, -#' -#' @references \cite{Y. Chen, R. Dwivedi, M. J. Wainwright and B. Yu, -#' \dQuote{Fast MCMC Sampling Algorithms on Polytopes,} \emph{Journal of Machine Learning Research,} 2018.} -#' -#' @references \cite{Lee, Yin Tat, Ruoqi Shen, and Kevin Tian, -#' \dQuote{"Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo,"} \emph{arXiv preprint arXiv:2002.04121}, 2020.} -#' -#' @references \cite{Shen, Ruoqi, and Yin Tat Lee, -#' \dQuote{"The randomized midpoint method for log-concave sampling.",} \emph{Advances in Neural Information Processing Systems}, 2019.} -#' -#' @references \cite{Augustin Chevallier, Sylvain Pion, Frederic Cazals, -#' \dQuote{"Hamiltonian Monte Carlo with boundary reflections, and application to polytope volume calculations,"} \emph{Research Report preprint hal-01919855}, 2018.} -#' -#' @return A \eqn{d\times n} matrix that contains, column-wise, the sampled points from the convex polytope P. -#' @examples -#' # uniform distribution from the 3d unit cube in H-representation using ball walk -#' P = gen_cube(3, 'H') -#' points = sample_points(P, n = 100, random_walk = list("walk" = "BaW", "walk_length" = 5)) -#' -#' # gaussian distribution from the 2d unit simplex in H-representation with variance = 2 -#' A = matrix(c(-1,0,0,-1,1,1), ncol=2, nrow=3, byrow=TRUE) -#' b = c(0,0,1) -#' P = Hpolytope$new(A,b) -#' points = sample_points(P, n = 100, distribution = list("density" = "gaussian", "variance" = 2)) -#' -#' # uniform points from the boundary of a 2-dimensional random H-polytope -#' P = gen_rand_hpoly(2,20) -#' points = sample_points(P, n = 100, random_walk = list("walk" = "BRDHR")) -#' -#' # For sampling from logconcave densities see the examples directory -#' -#' @export -sample_points <- function(P, n, random_walk = NULL, distribution = NULL, seed = NULL) { - .Call(`_volesti_sample_points`, P, n, random_walk, distribution, seed) -} - -#' Write a SDPA format file -#' -#' Outputs a spectrahedron (the matrices defining a linear matrix inequality) and a vector (the objective function) -#' to a SDPA format file. -#' -#' @param spectrahedron A spectrahedron in n dimensions; must be an object of class Spectrahedron -#' @param objectiveFunction A numerical vector of length n -#' @param outputFile Name of the output file -#' -#' @examples -#' \dontrun{ -#' A0 = matrix(c(-1,0,0,0,-2,1,0,1,-2), nrow=3, ncol=3, byrow = TRUE) -#' A1 = matrix(c(-1,0,0,0,0,1,0,1,0), nrow=3, ncol=3, byrow = TRUE) -#' A2 = matrix(c(0,0,-1,0,0,0,-1,0,0), nrow=3, ncol=3, byrow = TRUE) -#' lmi = list(A0, A1, A2) -#' S = Spectrahedron$new(lmi); -#' objFunction = c(1,1) -#' writeSdpaFormatFile(S, objFunction, "output.txt") -#' } -#' @export -writeSdpaFormatFile <- function(spectrahedron = NULL, objectiveFunction = NULL, outputFile = NULL) { - invisible(.Call(`_volesti_writeSdpaFormatFile`, spectrahedron, objectiveFunction, outputFile)) -} - -#' Read a SDPA format file -#' -#' @param inputFile Name of the input file -#' -#' @return A list with two named items: an item "matrices" which is a list of the matrices and an vector "objFunction" -#' -#' @examples -#' path = system.file('extdata', package = 'volesti') -#' l = loadSdpaFormatFile(paste0(path,'/sdpa_n2m3.txt')) -#' @export -loadSdpaFormatFile <- function(inputFile = NULL) { - .Call(`_volesti_loadSdpaFormatFile`, inputFile) -} - -#' The main function for volume approximation of a convex Polytope (H-polytope, V-polytope, zonotope or intersection of two V-polytopes). It returns a list with two elements: (a) the logarithm of the estimated volume and (b) the estimated volume -#' -#' For the volume approximation can be used three algorithms. Either CoolingBodies (CB) or SequenceOfBalls (SOB) or CoolingGaussian (CG). An H-polytope with \eqn{m} facets is described by a \eqn{m\times d} matrix \eqn{A} and a \eqn{m}-dimensional vector \eqn{b}, s.t.: \eqn{P=\{x\ |\ Ax\leq b\} }. A V-polytope is defined as the convex hull of \eqn{m} \eqn{d}-dimensional points which correspond to the vertices of P. A zonotope is desrcibed by the Minkowski sum of \eqn{m} \eqn{d}-dimensional segments. -#' -#' @param P A convex polytope. It is an object from class a) Hpolytope or b) Vpolytope or c) Zonotope or d) VpolytopeIntersection. -#' @param settings Optional. A list that declares which algorithm, random walk and values of parameters to use, as follows: -#' \itemize{ -#' \item{\code{algorithm} }{ A string to set the algorithm to use: a) \code{'CB'} for CB algorithm, b) \code{'SoB'} for SOB algorithm or b) \code{'CG'} for CG algorithm. The defalut algorithm is \code{'CB'}.} -#' \item{\code{error} }{ A numeric value to set the upper bound for the approximation error. The default value is \eqn{1} for SOB algorithm and \eqn{0.1} otherwise.} -#' \item{\code{random_walk} }{ A string that declares the random walk method: a) \code{'CDHR'} for Coordinate Directions Hit-and-Run, b) \code{'RDHR'} for Random Directions Hit-and-Run, c) \code{'BaW'} for Ball Walk, or \code{'BiW'} for Billiard walk. For CB algorithm the default walk is \code{'BiW'}. For CG and SOB algorithms the default walk is \code{'CDHR'} for H-polytopes and \code{'RDHR'} for the other representations.} -#' \item{\code{walk_length} }{ An integer to set the number of the steps for the random walk. The default value is \eqn{\lfloor 10 + d/10\rfloor} for \code{'SOB'} and \eqn{1} otherwise.} -#' \item{\code{win_len} }{ The length of the sliding window for CB or CG algorithm. The default value is \eqn{250} for CB with BiW and \eqn{400+3d^2} for CB and any other random walk and \eqn{500+4d^2} for CG.} -#' \item{\code{hpoly} }{ A boolean parameter to use H-polytopes in MMC of CB algorithm when the input polytope is a zonotope. The default value is \code{TRUE} when the order of the zonotope is \eqn{<5}, otherwise it is \code{FALSE}.} -#' } -#' @param rounding Optional. A string parameter to request a rounding method to be applied in the input polytope before volume computation: a) \code{'min_ellipsoid'}, b) \code{'svd'}, c) \code{'max_ellipsoid'} and d) \code{'none'} for no rounding. -#' @param seed Optional. A fixed seed for the number generator. -#' -#' @references \cite{I.Z.Emiris and V. Fisikopoulos, -#' \dQuote{Practical polytope volume approximation,} \emph{ACM Trans. Math. Soft.,} 2018.}, -#' @references \cite{A. Chalkis and I.Z.Emiris and V. Fisikopoulos, -#' \dQuote{Practical Volume Estimation by a New Annealing Schedule for Cooling Convex Bodies,} \emph{CoRR, abs/1905.05494,} 2019.}, -#' @references \cite{B. Cousins and S. Vempala, \dQuote{A practical volume algorithm,} \emph{Springer-Verlag Berlin Heidelberg and The Mathematical Programming Society,} 2015.} -#' -#' -#' @return The approximation of the volume of a convex polytope. -#' @examples -#' -#' # calling SOB algorithm for a H-polytope (5d unit simplex) -#' HP = gen_cube(5,'H') -#' pair_vol = volume(HP) -#' -#' # calling CG algorithm for a V-polytope (3d simplex) -#' VP = gen_simplex(3,'V') -#' pair_vol = volume(VP, settings = list("algorithm" = "CG")) -#' -#' # calling CG algorithm for a 2-dimensional zonotope defined as the Minkowski sum of 4 segments -#' Z = gen_rand_zonotope(2, 4) -#' pair_vol = volume(Z, settings = list("random_walk" = "RDHR", "walk_length" = 2)) -#' -#' @export -volume <- function(P, settings = NULL, rounding = NULL, seed = NULL) { - .Call(`_volesti_volume`, P, settings, rounding, seed) -} - -#' An internal Rccp function for the over-approximation of a zonotope -#' -#' @param Z A zonotope. -#' @param fit_ratio Optional. A boolean parameter to request the computation of the ratio of fitness. -#' @param settings Optional. A list that declares the values of the parameters of CB algorithm. -#' @param seed Optional. A fixed seed for the number generator. -#' -#' @keywords internal -#' -#' @return A List that contains a numerical matrix that describes the PCA approximation as a H-polytope and the ratio of fitness. -zono_approx <- function(Z, fit_ratio = NULL, settings = NULL, seed = NULL) { - .Call(`_volesti_zono_approx`, Z, fit_ratio, settings, seed) -} - diff --git a/R-proj/R/compute_indicators.R b/R-proj/R/compute_indicators.R deleted file mode 100644 index fe82cdee3..000000000 --- a/R-proj/R/compute_indicators.R +++ /dev/null @@ -1,104 +0,0 @@ -#' Compute an indicator for each time period that describes the state of a market. -#' -#' Given a matrix that contains row-wise the assets' returns and a sliding window \code{win_length}, this function computes an approximation of the joint distribution (copula, e.g. see \url{https://en.wikipedia.org/wiki/Copula_(probability_theory)}) between portfolios' return and volatility in each time period defined by \code{win_len}. -#' For each copula it computes an indicator: If the indicator is large it corresponds to a crisis period and if it is small it corresponds to a normal period. -#' In particular, the periods over which the indicator is greater than 1 for more than 60 consecutive sliding windows are warnings and for more than 100 are crisis. The sliding window is shifted by one day. -#' -#' @param returns A \eqn{d}-dimensional vector that describes the direction of the first family of parallel hyperplanes. -#' @param win_length Optional. The length of the sliding window. The default value is 60. -#' @param m Optional. The number of slices for the copula. The default value is 100. -#' @param n Optional. The number of points to sample. The default value is \eqn{5\cdot 10^5}. -#' @param nwarning Optional. The number of consecutive indicators larger than 1 required to declare a warning period. The default value is 60. -#' @param ncrisis Optional. The number of consecutive indicators larger than 1 required to declare a crisis period. The default value is 100. -#' @param seed Optional. A fixed seed for the number generator. -#' -#' @references \cite{L. Cales, A. Chalkis, I.Z. Emiris, V. Fisikopoulos, -#' \dQuote{Practical volume computation of structured convex bodies, and an application to modeling portfolio dependencies and financial crises,} \emph{Proc. of Symposium on Computational Geometry, Budapest, Hungary,} 2018.} -#' -#' @return A list that contains the indicators and the corresponding vector that label each time period with respect to the market state: a) normal, b) crisis, c) warning. -#' -#' @examples -#' # simple example on random asset returns -#' asset_returns = replicate(10, rnorm(14)) -#' market_states_and_indicators = compute_indicators(asset_returns, 10, 10, 10000, 2, 3) -#' -#' @export -compute_indicators <- function(returns, win_length = NULL, m = NULL, n = NULL, nwarning = NULL, ncrisis = NULL, seed = NULL) { - - if (is.null(win_length)) win_length = 60 - if (is.null(m)) m = 100 - if (is.null(n)) n = 500000 - if (is.null(nwarning)) nwarning = 60 - if (is.null(ncrisis)) ncrisis = 100 - - nrows = dim(returns)[1] - nassets = dim(returns)[2] - wl = win_length-1 - - indicators = c() - for (i in 1:(nrows-wl)) { - - Win=i:(i+wl) - E = cov(returns[Win,]) - - compRet = rep(1,nassets) - for (j in 1:nassets) { - for (k in Win) { - compRet[j] = compRet[j] * (1 + returns[k, j]) - } - compRet[j] = compRet[j] - 1 - } - - cop = copula(r1 = compRet, sigma = E, m = m, n = n, seed = seed) - blue_mass = 0 - red_mass = 0 - - for (row in 1:m) { - for (col in 1:m) { - if (row-col<=0.2*m && row-col>=-0.2*m) { - if (row+col<0.8*m || row+col>1.2*m) { - red_mass = red_mass + cop[row,col] - } - } else { - if (row+col>=0.8*m+1 && row+col<=1.2*m+1) { - blue_mass = blue_mass + cop[row,col] - } - } - } - } - indicators = c(indicators, blue_mass / red_mass) - } - - N = length(indicators) - - index = 0 - set_index = FALSE - col = rep("normal", N) - - for (i in 1:N) { - - if(indicators[i]>1 && !set_index){ - index = i - set_index = TRUE - } else if (indicators[i]<1) { - if(set_index){ - if(i-index > nwarning-1 && i-index <= ncrisis-1){ - col[index:(i-1)] = "warning" - } else if(i-index > ncrisis-1) { - col[index:(i-1)] = "crisis" - } - } - set_index = FALSE - } - } - if(set_index){ - if(N-index+1 > nwarning-1 && N-index+1 <= ncrisis-1){ - col[index:i] = "warning" - } else if(N-index+1 > ncrisis-1) { - col[index:i] = "crisis" - } - } - - return(list("indicators" = indicators, market_states = col)) - -} diff --git a/R-proj/R/estimate_lipschitz_constant.R b/R-proj/R/estimate_lipschitz_constant.R deleted file mode 100644 index c1fac15e0..000000000 --- a/R-proj/R/estimate_lipschitz_constant.R +++ /dev/null @@ -1,53 +0,0 @@ -#' Inverse weibull distribution PDF with location parameter -#' @param x The argument of the PDF -#' @param k The shape parameter -#' @param lambda The scale parameter -#' @param theta The location parameter -#' -#' @return The value of the PDF of an Inverse Weibull distribution with parameters k, lambda, theta evaluated at x -#' @export -dinvweibull_with_loc <- function (x, k, lambda, theta) { - return ((k / lambda) * ((x - theta) / lambda)^(k - 1) * exp(- ((x - theta) / lambda)^k) * as.double(x >= 0)) -} - -#' Inverse weibull distribution CDF with location parameter -#' @param q The argument of the CDF -#' @param k The shape parameter -#' @param lambda The scale parameter -#' @param theta The location parameter -#' -#' @return The value of the CDF of an Inverse Weibull distribution with parameters k, lambda, theta evaluated at q -#' @export -pinvweibull_with_loc <- function (q, k, lambda, theta) { - return ((1 - exp(-((q - theta) / lambda)^k)) * as.double(q >= 0)) -} - - -#' Estimate the Lipschitz Constant of a function f -#' -#' @param f Function whose Lipschitz constant is to be estimated -#' @param P Domain of f (a convex polytope) -#' @param n Number of samples to take -#' -#' The procedure draws n uniform samples from P and evaluates the Lipschitz -#' constant at subsequent samples (where the sampler moves to a new point), -#' It then returns the maximum observation -#' -#' @return An estimate of the Lipschitz constant -#' -#' @export -estimtate_lipschitz_constant <- function (f, P, n) { - points = volesti::sample_points(P, n = 1000, random_walk = list("walk" = "BaW", "walk_length" = 1)) - l = matrix(0, 1, n-1) - - for (i in seq(n-1)) { - l[1, i] <- norm(as.matrix(f(points[,i+1])) - as.matrix(f(points[,i])), type='F') / norm(as.matrix(points[,i+1]) - as.matrix(points[,i]), type='F') - } - - na_cols <- is.nan(l[1,]) - - l <- as.matrix(l[1, !na_cols]) - - # TODO Implement weibull distribution fitting method to estimate the Lipschitz constant - return(max(l)) -} \ No newline at end of file diff --git a/R-proj/R/file_to_polytope.R b/R-proj/R/file_to_polytope.R deleted file mode 100644 index 19dd61acf..000000000 --- a/R-proj/R/file_to_polytope.R +++ /dev/null @@ -1,113 +0,0 @@ -#' function to get an ine or an ext file and returns the corresponding polytope -#' -#' For an ".ine" file it generates the corresponding H-polytope. For an ".ext" file it generates the corresponding V-polytope or zonotope. -#' For more details on those file formats see \url{https://github.com/GeomScale/volume_approximation/blob/develop/doc/cpp_interface.md#polytope-input}. -#' -#' @param path A string that containes the path to an ine or a ext file. The ine file desrcibes a H-polytope and ext file describes a V-polytope or a zonotope. -#' @param zonotope A boolean parameter. It has to be TRUE when the path leads to an .ext file that describes a zonotope. -#' -#' @return A polytope class. If the path corresponds to an ine file then the return value represents a H-polytope. If it corresponds to an ext file the return value represents a V-polytope (default choice) or a zonotope if the second argument is TRUE. -#' -#' @export -#' @useDynLib volesti, .registration=TRUE -#' @importFrom Rcpp evalCpp -#' @importFrom Rcpp loadModule -#' @importFrom "utils" "read.csv" -#' @importFrom "stats" "cov" -#' @importFrom "methods" "new" -#' @exportPattern "^[[:alpha:]]+" -file_to_polytope <- function(path, zonotope = FALSE){ - - ineorext=substr(path, start = nchar(path) - 2, stop = nchar(path)) - if(ineorext!="ine" && ineorext!="ext") { - stop("Only ine or ext files can be handled by this function!") - } - P = read.csv(path) - r = as.character(P[3,1]) - count_sp = 1 - str = "" - beg = 0 - for (j in 1:nchar(r)) { - if (substr(r, start=j, stop=j) == " ") { - beg = beg + 1 - } else { - break - } - } - for (i in seq(from= beg + 1, to=nchar(r), by=1)) { - if (substr(r, start=i, stop=i) == " ") { - if (count_sp == 1) { - m = as.numeric(str) - str = "" - count_sp = count_sp + 1 - } else { - d = as.numeric(str) - str = "" - break - } - } else { - str = paste0(str, substr(r, start=i, stop=i)) - } - } - A = rep(0,d) - A[1] = m - A[2] = d - newrow = rep(0,d) - for (i in 4:(dim(P)[1] - 2)) { - r = P[i,1] - r = as.character(r) - str = "" - count = 1 - beg = 0 - for (j in 1:nchar(r)) { - if(substr(r, start=j, stop=j)==" "){ - beg = beg + 1 - } else { - break - } - } - sp_bef = FALSE - for (j in seq(from=beg + 1, to=nchar(r), by=1)) { - if (substr(r, start=j, stop=j) == " "){ - if (sp_bef) { - next - } - sp_bef = TRUE - newrow[count] = as.numeric(str) - str = "" - count = count + 1 - } else { - str = paste0(str, substr(r, start=j, stop=j)) - sp_bef = FALSE - if (j == nchar(r)) { - newrow[count] = as.numeric(str) - } - } - } - A = rbind(A,newrow) - newrow = rep(0,d) - } - A = matrix(A, ncol=dim(A)[2]) # now matrix A is in ine or ext format - - # remove first row - A = A[-c(1),] - - # first column is the vector b - b = A[,1] - - # remove first column - A2 = A[,-c(1)] - - if(ineorext=="ine") { - P = Hpolytope$new(-A2,b) - } else { - if(!missing(zonotope)){ - if(zonotope) { - P = Zonotope$new(A2) - return(P) - } - } - P = Vpolytope$new(A2) - } - return(P) -} diff --git a/R-proj/R/gen_birkhoff.R b/R-proj/R/gen_birkhoff.R deleted file mode 100644 index 935f637e2..000000000 --- a/R-proj/R/gen_birkhoff.R +++ /dev/null @@ -1,28 +0,0 @@ -#' Generator function for Birkhoff polytope -#' -#' This function can be used to generate the full dimensional \eqn{n}-Birkhoff polytope in H-representation. -#' The dimension of the generated polytope is \eqn{(n-1)^2}. -#' -#' @param n The order of the Birkhoff polytope -#' -#' @return A polytope class representing the full dimensional \eqn{n}-Birkhoff polytope in H-representation. -#' @examples -#' # generate the Birkhoff polytope of order 5 -#' P = gen_birkhoff(5) -#' @export -gen_birkhoff <- function(n) { - - kind_gen = 7 - m_gen = 0 - - Mat = poly_gen(kind_gen, FALSE, FALSE, n, m_gen) - - # first column is the vector b - b = Mat[,1] - Mat = Mat[,-c(1)] - - P = Hpolytope$new(Mat, b) - - return(P) - -} diff --git a/R-proj/R/gen_cross.R b/R-proj/R/gen_cross.R deleted file mode 100644 index d782f3991..000000000 --- a/R-proj/R/gen_cross.R +++ /dev/null @@ -1,41 +0,0 @@ -#' Generator function for cross polytopes -#' -#' This function generates the \eqn{d}-dimensional cross polytope in H- or V-representation. -#' -#' @param dimension The dimension of the cross polytope. -#' @param representation A string to declare the representation. It has to be \code{'H'} for H-representation or \code{'V'} for V-representation. -#' -#' @return A polytope class representing a cross polytope in H- or V-representation. -#' @examples -#' # generate a 10-dimensional cross polytope in H-representation -#' P = gen_cross(5, 'H') -#' -#' # generate a 15-dimension cross polytope in V-representation -#' P = gen_cross(15, 'V') -#' @export -gen_cross <- function(dimension, representation) { - - kind_gen = 2 - m_gen = 0 - if (representation == "V") { - Vpoly_gen = TRUE - } else if (representation == "H") { - Vpoly_gen = FALSE - } else { - stop('Not a known representation.') - } - - Mat = poly_gen(kind_gen, Vpoly_gen, FALSE, dimension, m_gen) - - # first column is the vector b - b = Mat[,1] - Mat = Mat[,-c(1)] - - if (Vpoly_gen) { - P = Vpolytope$new(Mat, 2^dimension / prod(1:dimension)) - } else { - P = Hpolytope$new(-Mat, b, 2^dimension / prod(1:dimension)) - } - - return(P) -} diff --git a/R-proj/R/gen_cube.R b/R-proj/R/gen_cube.R deleted file mode 100644 index bcbf1f025..000000000 --- a/R-proj/R/gen_cube.R +++ /dev/null @@ -1,41 +0,0 @@ -#' Generator function for hypercubes -#' -#' This function generates the \eqn{d}-dimensional unit hypercube \eqn{[-1,1]^d} in H- or V-representation. -#' -#' @param dimension The dimension of the hypercube -#' @param representation A string to declare the representation. It has to be \code{'H'} for H-representation or \code{'V'} for V-representation. -#' -#' @return A polytope class representing the unit \eqn{d}-dimensional hypercube in H- or V-representation. -#' @examples -#' # generate a 10-dimensional hypercube in H-representation -#' P = gen_cube(10, 'H') -#' -#' # generate a 15-dimension hypercube in V-representation -#' P = gen_cube(5, 'V') -#' @export -gen_cube <- function(dimension, representation) { - - kind_gen = 1 - m_gen = 0 - if (representation == "V") { - Vpoly_gen = TRUE - } else if (representation == "H") { - Vpoly_gen = FALSE - } else { - stop('Not a known representation.') - } - - Mat = poly_gen(kind_gen, Vpoly_gen, FALSE, dimension, m_gen) - - # first column is the vector b - b = Mat[,1] - Mat = Mat[,-c(1)] - if (Vpoly_gen) { - P = Vpolytope$new(Mat, 2^dimension) - } else { - P = Hpolytope$new(-Mat, b, 2^dimension) - } - - return(P) - -} diff --git a/R-proj/R/gen_prod_simplex.R b/R-proj/R/gen_prod_simplex.R deleted file mode 100644 index a0505880c..000000000 --- a/R-proj/R/gen_prod_simplex.R +++ /dev/null @@ -1,29 +0,0 @@ -#' Generator function for product of simplices -#' -#' This function generates a \eqn{2d}-dimensional polytope that is defined as the product of two \eqn{d}-dimensional unit simplices in H-representation. -#' -#' @param dimension The dimension of the simplices. -#' -#' @return A polytope class representing the product of the two \eqn{d}-dimensional unit simplices in H-representation. -#' -#' @examples -#' # generate a product of two 5-dimensional simplices. -#' P = gen_prod_simplex(5) -#' @export -gen_prod_simplex <- function(dimension) { - - kind_gen = 4 - m_gen = 0 - Vpoly_gen = FALSE - - Mat = poly_gen(kind_gen, Vpoly_gen, FALSE, dimension, m_gen) - - # first column is the vector b - b = Mat[,1] - Mat = Mat[,-c(1)] - - P = Hpolytope$new(-Mat, b, (1/prod(1:dimension))^2) - - return(P) - -} diff --git a/R-proj/R/gen_rand_hpoly.R b/R-proj/R/gen_rand_hpoly.R deleted file mode 100644 index 2785a28b4..000000000 --- a/R-proj/R/gen_rand_hpoly.R +++ /dev/null @@ -1,28 +0,0 @@ -#' Generator function for random H-polytopes -#' -#' This function generates a \eqn{d}-dimensional polytope in H-representation with \eqn{m} facets. We pick \eqn{m} random hyperplanes tangent on the \eqn{d}-dimensional unit hypersphere as facets. -#' -#' @param dimension The dimension of the convex polytope. -#' @param nfacets The number of the facets. -#' @param seed Optional. A fixed seed for the generator. -#' -#' @return A polytope class representing a H-polytope. -#' @examples -#' # generate a 10-dimensional polytope with 50 facets -#' P = gen_rand_hpoly(10, 50) -#' @export -gen_rand_hpoly <- function(dimension, nfacets, seed = NULL) { - - kind_gen = 6 - Vpoly_gen = FALSE - - Mat = poly_gen(kind_gen, Vpoly_gen, FALSE, dimension, nfacets, seed) - - # first column is the vector b - b = Mat[,1] - Mat = Mat[,-c(1)] - - P = Hpolytope$new(Mat, b) - - return(P) -} diff --git a/R-proj/R/gen_rand_vpoly.R b/R-proj/R/gen_rand_vpoly.R deleted file mode 100644 index 31cc561c0..000000000 --- a/R-proj/R/gen_rand_vpoly.R +++ /dev/null @@ -1,36 +0,0 @@ -#' Generator function for random V-polytopes -#' -#' This function generates a \eqn{d}-dimensional polytope in V-representation with \eqn{m} vertices. We pick \eqn{m} random points from the boundary of the \eqn{d}-dimensional unit hypersphere as vertices. -#' -#' @param dimension The dimension of the convex polytope. -#' @param nvertices The number of the vertices. -#' @param generator The body that the generator samples uniformly the vertices from: (a) 'cube' or (b) 'sphere'. -#' @param seed Optional. A fixed seed for the generator. -#' -#' @return A polytope class representing a V-polytope. -#' @examples -#' # generate a 10-dimensional polytope defined as the convex hull of 25 random vertices -#' P = gen_rand_vpoly(10, 25) -#' @export -gen_rand_vpoly <- function(dimension, nvertices, generator = NULL, seed = NULL) { - - kind_gen = 4 - - if(!is.null(generator)){ - if (generator == 'cube'){ - kind_gen = 5 - } else if (generator != 'sphere') { - stop("Wrong generator!") - } - } - - Mat = poly_gen(kind_gen, TRUE, FALSE, dimension, nvertices, seed) - - # first column is the vector b - b = Mat[,1] - Mat = Mat[,-c(1)] - - P = Vpolytope$new(Mat) - - return(P) -} diff --git a/R-proj/R/gen_rand_zonotope.R b/R-proj/R/gen_rand_zonotope.R deleted file mode 100644 index 3a2c8c1b2..000000000 --- a/R-proj/R/gen_rand_zonotope.R +++ /dev/null @@ -1,40 +0,0 @@ -#' Generator function for zonotopes -#' -#' This function generates a random \eqn{d}-dimensional zonotope defined by the Minkowski sum of \eqn{m} \eqn{d}-dimensional segments. -#' The function considers \eqn{m} random directions in \eqn{R^d}. There are three strategies to pick the length of each segment: a) it is uniformly sampled from \eqn{[0,100]}, b) it is random from \eqn{\mathcal{N}(50,(50/3)^2)} truncated to \eqn{[0,100]}, c) it is random from \eqn{Exp(1/30)} truncated to \eqn{[0,100]}. -#' -#' @param dimension The dimension of the zonotope. -#' @param nsegments The number of segments that generate the zonotope. -#' @param generator The distribution to pick the length of each segment from \eqn{[0,100]}: (a) 'uniform', (b) 'gaussian' or (c) 'exponential'. -#' @param seed Optional. A fixed seed for the generator. -#' -#' @return A polytope class representing a zonotope. -#' -#' @examples -#' # generate a 10-dimensional zonotope defined by the Minkowski sum of 20 segments -#' P = gen_rand_zonotope(10, 20) -#' @export -gen_rand_zonotope <- function(dimension, nsegments, generator = NULL, seed = NULL) { - - kind_gen = 1 - - if (!is.null(generator)) { - if (generator == 'gaussian') { - kind_gen = 2 - } else if (generator == 'exponential') { - kind_gen = 3 - } else if (generator != 'uniform'){ - stop("Wrong generator!") - } - } - - Mat = poly_gen(kind_gen, FALSE, TRUE, dimension, nsegments, seed) - - # first column is the vector b - b = Mat[,1] - Mat = Mat[,-c(1)] - - P = Zonotope$new(Mat) - - return(P) -} diff --git a/R-proj/R/gen_simplex.R b/R-proj/R/gen_simplex.R deleted file mode 100644 index aa2ca4a6a..000000000 --- a/R-proj/R/gen_simplex.R +++ /dev/null @@ -1,41 +0,0 @@ -#' Generator function for simplices -#' -#' This function generates the \eqn{d}-dimensional unit simplex in H- or V-representation. -#' -#' @param dimension The dimension of the unit simplex. -#' @param representation A string to declare the representation. It has to be \code{'H'} for H-representation or \code{'V'} for V-representation. -#' -#' @return A polytope class representing the \eqn{d}-dimensional unit simplex in H- or V-representation. -#' @examples -#' # generate a 10-dimensional simplex in H-representation -#' PolyList = gen_simplex(10, 'H') -#' -#' # generate a 20-dimensional simplex in V-representation -#' P = gen_simplex(20, 'V') -#' @export -gen_simplex <- function(dimension, representation) { - - kind_gen = 3 - m_gen = 0 - if (representation == "V") { - Vpoly_gen = TRUE - } else if (representation == "H") { - Vpoly_gen = FALSE - } else { - stop('Not a known representation.') - } - - Mat = poly_gen(kind_gen, Vpoly_gen, FALSE, dimension, m_gen) - - # first column is the vector b - b = Mat[,1] - Mat = Mat[,-c(1)] - - if (Vpoly_gen) { - P = Vpolytope$new(Mat, 1/prod(1:dimension)) - } else { - P = Hpolytope$new(-Mat, b, 1/prod(1:dimension)) - } - - return(P) -} diff --git a/R-proj/R/gen_skinny_cube.R b/R-proj/R/gen_skinny_cube.R deleted file mode 100644 index 0a1df0eb0..000000000 --- a/R-proj/R/gen_skinny_cube.R +++ /dev/null @@ -1,28 +0,0 @@ -#' Generator function for skinny hypercubes -#' -#' This function generates a \eqn{d}-dimensional skinny hypercube \eqn{[-1,1]^{d-1}\times [-100,100]}. -#' -#' @param dimension The dimension of the skinny hypercube. -#' -#' @return A polytope class representing the \eqn{d}-dimensional skinny hypercube in H-representation. -#' -#' @examples -#' # generate a 10-dimensional skinny hypercube. -#' P = gen_skinny_cube(10) -#' @export -gen_skinny_cube <- function(dimension) { - - kind_gen = 5 - m_gen = 0 - Vpoly_gen = FALSE - - Mat = poly_gen(kind_gen, Vpoly_gen, FALSE, dimension, m_gen) - - # first column is the vector b - b = Mat[,1] - Mat = Mat[,-c(1)] - - P = Hpolytope$new(-Mat, b, 2^(dimension -1)*200) - - return(P) -} diff --git a/R-proj/R/read_sdpa_file.R b/R-proj/R/read_sdpa_file.R deleted file mode 100644 index 83c10c528..000000000 --- a/R-proj/R/read_sdpa_file.R +++ /dev/null @@ -1,26 +0,0 @@ -#' Read a SDPA format file -#' -#' Read a SDPA format file and return a spectrahedron (an object of class Spectrahedron) which is defined by -#' the linear matrix inequality in the input file, and the objective function. -#' -#' @param path Name of the input file -#' -#' @return A list with two named items: an item "matrices" which is an object of class Spectrahedron and an vector "objFunction" -#' -#' @examples -#' path = system.file('extdata', package = 'volesti') -#' l = readSdpaFormatFile(paste0(path,'/sdpa_n2m3.txt')) -#' Spectrahedron = l$spectrahedron -#' objFunction = l$objFunction -#' @export -#' @useDynLib volesti, .registration=TRUE -#' @importFrom Rcpp evalCpp -#' @importFrom Rcpp loadModule -#' @importFrom "methods" "new" -#' @exportPattern "^[[:alpha:]]+" -readSdpaFormatFile <- function(path){ - l = loadSdpaFormatFile(path) - S = Spectrahedron$new(l$matrices) - - return(list("spectrahedron"=S, "objFunction"= l$objFunction)) -} diff --git a/R-proj/R/rotate_polytope.R b/R-proj/R/rotate_polytope.R deleted file mode 100644 index 12ce246b8..000000000 --- a/R-proj/R/rotate_polytope.R +++ /dev/null @@ -1,57 +0,0 @@ -#' Apply a random rotation to a convex polytope (H-polytope, V-polytope, zonotope or intersection of two V-polytopes) -#' -#' Given a convex H- or V- polytope or a zonotope or an intersection of two V-polytopes as input, this function applies (a) a random rotation or (b) a given rotation by an input matrix \eqn{T}. -#' -#' @param P A convex polytope. It is an object from class (a) Hpolytope, (b) Vpolytope, (c) Zonotope, (d) intersection of two V-polytopes. -#' @param T Optional. A \eqn{d\times d} rotation matrix. -#' @param seed Optional. A fixed seed for the random linear map generator. -#' -#' @return A list that contains the rotated polytope and the matrix \eqn{T} of the linear transformation. -#' -#' @details Let \eqn{P} be the given polytope and \eqn{Q} the rotated one and \eqn{T} be the matrix of the linear transformation. -#' \itemize{ -#' \item{If \eqn{P} is in H-representation and \eqn{A} is the matrix that contains the normal vectors of the facets of \eqn{Q} then \eqn{AT} contains the normal vactors of the facets of \eqn{P}.} -#' \item{If \eqn{P} is in V-representation and \eqn{V} is the matrix that contains column-wise the vertices of \eqn{Q} then \eqn{T^TV} contains the vertices of \eqn{P}.} -#' \item{If \eqn{P} is a zonotope and \eqn{G} is the matrix that contains column-wise the generators of \eqn{Q} then \eqn{T^TG} contains the generators of \eqn{P}.} -#' \item{If \eqn{M} is a matrix that contains column-wise points in \eqn{Q} then \eqn{T^TM} contains points in \eqn{P}.} -#' } -#' @examples -#' # rotate a H-polytope (2d unit simplex) -#' P = gen_simplex(2,'H') -#' poly_matrix_list = rotate_polytope(P) -#' -#' # rotate a V-polytope (3d cube) -#' P = gen_cube(3, 'V') -#' poly_matrix_list = rotate_polytope(P) -#' -#' # rotate a 5-dimensional zonotope defined by the Minkowski sum of 15 segments -#' Z = gen_rand_zonotope(3,6) -#' poly_matrix_list = rotate_polytope(Z) -#' @export -rotate_polytope <- function(P, T = NULL, seed = NULL){ - - #call rcpp rotating function - Mat = rotating(P, T, seed) - - n = P$dimension - m=dim(Mat)[2]-n - Tr = Mat[,-c(1:(dim(Mat)[2]-n))] - Tr = Tr[1:n, 1:n] - Mat = t(Mat[,1:m]) - - # first column is the vector b - b = Mat[,1] - - # remove first column - A = Mat[,-c(1)] - - type = P$type - if (type == 2) { - PP = Vpolytope$new(A) - }else if (type == 3) { - PP = Zonotope$new(A) - } else { - PP = Hpolytope$new(A, b) - } - return(list("P" = PP, "T" = Tr)) -} diff --git a/R-proj/R/round_polytope.R b/R-proj/R/round_polytope.R deleted file mode 100644 index f95649c66..000000000 --- a/R-proj/R/round_polytope.R +++ /dev/null @@ -1,60 +0,0 @@ -#' Apply rounding to a convex polytope (H-polytope, V-polytope or a zonotope) -#' -#' Given a convex H or V polytope or a zonotope as input this function brings the polytope in rounded position based on minimum volume enclosing ellipsoid of a pointset. -#' -#' @param P A convex polytope. It is an object from class (a) Hpolytope or (b) Vpolytope or (c) Zonotope. -#' @param method Optional. The method to use for rounding, a) \code{'min_ellipsoid'} for the method based on mimimmum volume enclosing ellipsoid of a dataset, b) \code{'max_ellipsoid'} for the method based on maximum volume enclosed ellipsoid, (c) \code{'isotropy'} for the method based on svd decomposition. The default method is \code{'mee'} for all the representations. -#' @param seed Optional. A fixed seed for the number generator. -#' -#' @return A list with 4 elements: (a) a polytope of the same class as the input polytope class and (b) the element "T" which is the matrix of the inverse linear transformation that is applied on the input polytope, (c) the element "shift" which is the opposite vector of that which has shifted the input polytope, (d) the element "round_value" which is the determinant of the square matrix of the linear transformation that is applied on the input polytope. -#' -#' @references \cite{I.Z.Emiris and V. Fisikopoulos, -#' \dQuote{Practical polytope volume approximation,} \emph{ACM Trans. Math. Soft.,} 2018.}, -#' @references \cite{Michael J. Todd and E. Alper Yildirim, -#' \dQuote{On Khachiyan’s Algorithm for the Computation of Minimum Volume Enclosing Ellipsoids,} \emph{Discrete Applied Mathematics,} 2007.} -#' @references \cite{B. Cousins and S. Vempala, -#' \dQuote{A practical volume algorithm,} \emph{Math. Prog. Comp.,} 2016.}, -#' @references \cite{Yin Zhang and Liyan Gao, -#' \dQuote{On Numerical Solution of the Maximum Volume Ellipsoid Problem,} \emph{SIAM Journal on Optimization,} 2003.}, -#' -#' -#' @examples -#' # round a 5d skinny cube -#' P = gen_skinny_cube(5) -#' listHpoly = round_polytope(P) -#' -#' # round a V-polytope (3d unit cube) -#' P = gen_cube(3, 'V') -#' ListVpoly = round_polytope(P) -#' -#' # round a 2-dimensional zonotope defined by 6 generators -#' Z = gen_rand_zonotope(2,6) -#' ListZono = round_polytope(Z) -#' @export -round_polytope <- function(P, method = NULL, seed = NULL){ - - ret_list = rounding(P, method, seed) - - #get the matrix that describes the polytope - Mat = ret_list$Mat - - # first column is the vector b - b = Mat[,1] - - # remove first column - A = Mat[,-c(1)] - - type = P$type - if (type == 2) { - PP = list("P" = Vpolytope$new(A), "T" = ret_list$T, "shift" = ret_list$shift, "round_value" = ret_list$round_value) - }else if (type == 3) { - PP = list("P" = Zonotope$new(A), "T" = ret_list$T, "shift" = ret_list$shift, "round_value" = ret_list$round_value) - } else { - if (dim(P$Aeq)[1] > 0){ - PP = list("P" = Hpolytope$new(A,b), "T" = ret_list$T, "shift" = ret_list$shift, "round_value" = ret_list$round_value, "N" = ret_list$N, "N_shift" = ret_list$N_shift, "svd_prod" = ret_list$svd_prod) - } else { - PP = list("P" = Hpolytope$new(A,b), "T" = ret_list$T, "shift" = ret_list$shift, "round_value" = ret_list$round_value) - } - } - return(PP) -} diff --git a/R-proj/R/zonotope_approximation.R b/R-proj/R/zonotope_approximation.R deleted file mode 100644 index 8c56f6d23..000000000 --- a/R-proj/R/zonotope_approximation.R +++ /dev/null @@ -1,42 +0,0 @@ -#' A function to over-approximate a zonotope with PCA method and to evaluate the approximation by computing a ratio of fitness. -#' -#' For the evaluation of the PCA method the exact volume of the approximation body is computed and the volume of the input zonotope is computed by CoolingBodies algorithm. The ratio of fitness is \eqn{R=vol(P) / vol(P_{red})}, where \eqn{P_{red}} is the approximate polytope. -#' -#' @param Z A zonotope. -#' @param fit_ratio Optional. A boolean parameter to request the computation of the ratio of fitness. -#' @param settings Optional. A list that declares the values of the parameters of CB algorithm as follows: -#' \itemize{ -#' \item{\code{error} }{ A numeric value to set the upper bound for the approximation error. The default value is \eqn{0.1}.} -#' \item{\code{walk_length} }{ An integer to set the number of the steps for the random walk. The default value is \eqn{1}.} -#' \item{\code{win_len} }{ The length of the sliding window for CB algorithm. The default value is \eqn{200}.} -#' \item{\code{hpoly} }{ A boolean parameter to use H-polytopes in MMC of CB algorithm. The default value is \code{TRUE} when the order of the zonotope is \eqn{<5}, otherwise it is \code{FALSE}.} -#' } -#' @param seed Optional. A fixed seed for the number generator. -#' -#' @return A list that contains the approximation body in H-representation and the ratio of fitness -#' -#' @references \cite{A.K. Kopetzki and B. Schurmann and M. Althoff, -#' \dQuote{Methods for Order Reduction of Zonotopes,} \emph{IEEE Conference on Decision and Control,} 2017.} -#' -#' @examples -#' # over-approximate a 2-dimensional zonotope with 10 generators and compute the ratio of fitness -#' Z = gen_rand_zonotope(2,12) -#' retList = zonotope_approximation(Z = Z) -#' -#' @export -zonotope_approximation <- function(Z, fit_ratio = NULL, settings = NULL, seed = NULL){ - - ret_list = zono_approx(Z, fit_ratio, settings, seed) - - Mat = ret_list$Mat - - # first column is the vector b - b = Mat[,1] - - # remove first column - A = Mat[,-c(1)] - PP = list("P" = Hpolytope$new(A,b), "fit_ratio" = ret_list$fit_ratio) - - return(PP) - -} diff --git a/R-proj/R/zzz.R b/R-proj/R/zzz.R deleted file mode 100644 index 564a20b9c..000000000 --- a/R-proj/R/zzz.R +++ /dev/null @@ -1,11 +0,0 @@ -## Up until R 2.15.0, the require("methods") is needed but (now) -## triggers an warning from R CMD check -#.onLoad <- function(libname, pkgname){ -# #require("methods") ## needed with R <= 2.15.0 -# loadRcppModules() - -## For R 2.15.1 and later this also works. 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a/R-proj/examples/logconcave/generalized_hyperbolic.R b/R-proj/examples/logconcave/generalized_hyperbolic.R deleted file mode 100644 index e0f9b90ce..000000000 --- a/R-proj/examples/logconcave/generalized_hyperbolic.R +++ /dev/null @@ -1,65 +0,0 @@ -# VolEsti (volume computation and sampling library) - -# Copyright (c) 2012-2020 Vissarion Fisikopoulos -# Copyright (c) 2018-2020 Apostolos Chalkis -# Copyright (c) 2020-2020 Marios Papachristou - -# Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2020 program. - -# Licensed under GNU LGPL.3, see LICENCE file - -# Example script for sampling from a Generalized Hyperbolic density - -# Import required libraries -library(ggplot2) -library(volesti) -library(numDeriv) -library(GeneralizedHyperbolic) - -A = matrix(c(1, -1), ncol=1, nrow=2, byrow=TRUE) -b = c(4,4) - -f <- function(x) (-log(dghyp(x))) -grad_f <- function(x) (-ddghyp(x)/dghyp(x)) - -x_min = matrix(0, 1, 1) - -# Create domain of truncation -P <- volesti::Hpolytope$new(A, b) - -# Smoothness and strong-convexity -L <- estimtate_lipschitz_constant(grad_f, P, 1000) -m <- L - -# Warm start point from truncated Gaussian -warm_start <- sample_points(P, n = 1, random_walk = list("nburns" = 5000), distribution = list("density" = "gaussian", "variance" = 1/L, "mode" = x_min)) - -# Sample points -n_samples <- 10000 -n_burns <- n_samples / 2 - -pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "HMC", "step_size" = 0.5, "nburns" = n_burns, "walk_length" = 1, "solver" = "leapfrog", "starting_point" = warm_start[,1]), distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f, "L_" = L, "m" = m)) - -# Plot histogram -hist(pts, - probability=TRUE, - breaks = 100, - border="blue", - main="Genrealized Hyperbolic Density with lambda = 1, alpha = 1, beta = 0, delta = 1, mu = 0", - xlab="Samples", - ylab="Density" -) - -cat("Sample mean is: ") -sample_mean <- mean(pts) -cat(sample_mean) -cat("\n") -cat("Sample variance is: ") -sample_variance <- mean((pts - sample_mean)^2) -cat(sample_variance) - -n_ess = min(ess(pts)) -psrf = max(psrf_univariate(pts)) - -cat("\nEffective sample size: ", n_ess, append=TRUE) -cat("\nPSRF: ", psrf, append=TRUE) diff --git a/R-proj/examples/logconcave/metabolic.R b/R-proj/examples/logconcave/metabolic.R deleted file mode 100644 index 7785de0d7..000000000 --- a/R-proj/examples/logconcave/metabolic.R +++ /dev/null @@ -1,111 +0,0 @@ -# VolEsti (volume computation and sampling library) - -# Copyright (c) 2012-2020 Vissarion Fisikopoulos -# Copyright (c) 2018-2020 Apostolos Chalkis -# Copyright (c) 2020-2020 Marios Papachristou - -# Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2020 program. - -# Licensed under GNU LGPL.3, see LICENCE file - -# Example script for using the logconcave sampling methods - -# Import required libraries -library(ggplot2) -library(volesti) -library(R.matlab) - -# Sampling from logconcave density example - -# Helper function -norm_vec <- function(x) sqrt(sum(x^2)) - - -# Load polytopes from mat file -metabolic_polytope_mat <- readMat('./data/polytope_e_coli.mat') -A <- as.matrix(metabolic_polytope_mat$polytope[[1]]) -b <- as.matrix(metabolic_polytope_mat$polytope[[2]]) -center <- as.matrix(metabolic_polytope_mat$polytope[[3]]) -radius <- as.numeric(metabolic_polytope_mat$polytope[[4]]) -sigma <- 1 -dimension <- dim(A)[2] - - -# Negative log-probability oracle -f <- function(x) (norm_vec(x)^2 / (2 * sigma^2)) - -# Negative log-probability gradient oracle -grad_f <- function(x) (x / sigma^2) - - -# Smoothness and strong-convexity -L <- 1 / sigma^2 -m <- 1 / sigma^2 - -# Center polytope -b_new <- b - A %*% center - -# Create volesti polytope -P <- Hpolytope$new(A = A, b = c(b_new)) - -# Rounding -#Tr <- rounding(H) - -#P <- Hpolytope$new(A = Tr$Mat[1:nrow(Tr$Mat), 2:ncol(Tr$Mat)], b = Tr$Mat[,1]) - -# Center is origin (after shift) -x_min = matrix(0, dimension, 1) - -# Generate samples with HNR -start_time <- Sys.time() -rdhr_samples <- sample_points(P, n = 10, random_walk = list("walk" = "RDHR", "nburns" = 10, "walk_length" = 1), distribution = list("density" = "gaussian", "variance" = 1/L, "mode" = x_min)) -end_time <- Sys.time() - -# Calculate Effective Sample size -rdhr_ess = ess(rdhr_samples) -min_ess <- min(rdhr_ess) - -# Calculate PSRF -rdhr_psrfs = psrf_univariate(rdhr_samples) -max_psrf = max(rdhr_psrfs) -elapsed_time <- end_time - start_time - -# Print results -cat('Min Effective Sample Size: ') -cat(min_ess) -cat('\n') -cat('Maximum PSRF: ') -cat(max_psrf) -cat('\n') -cat('Time per independent sample: ') -cat(elapsed_time / min_ess) -cat('sec') - -outfile <- '/home/marios/samples_hnr_iAB_PLT_283.txt' - -write.table(rdhr_samples, file=outfile, row.names=FALSE, col.names=FALSE) - -start_time <- Sys.time() -hmc_samples <- sample_points(P, n = 10, random_walk = list("walk" = "HMC", "step_size" = 0.07, "nburns" = 10, "walk_length" = 30, "solver" = "leapfrog", "starting_point" = rdhr_samples[, ncol(rdhr_samples)]), distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f, "L_" = L, "m" = m)) -end_time <- Sys.time() - -# Calculate Effective Sample size -hmc_ess = ess(hmc_samples) -min_ess <- min(hmc_ess) - -# Calculate PSRF -hmc_psrfs = psrf_univariate(hmc_samples) -max_psrf = max(hmc_psrfs) -elapsed_time <- end_time - start_time - -# Print results -cat('HMC\n') -cat('Min Effective Sample Size: ') -cat(min_ess) -cat('\n') -cat('Maximum PSRF: ') -cat(max_psrf) -cat('\n') -cat('Time per independent sample: ') -cat(elapsed_time / min_ess) -cat('sec') diff --git a/R-proj/examples/logconcave/nuts_rand_poly.R b/R-proj/examples/logconcave/nuts_rand_poly.R deleted file mode 100644 index 00400ba72..000000000 --- a/R-proj/examples/logconcave/nuts_rand_poly.R +++ /dev/null @@ -1,55 +0,0 @@ -# VolEsti (volume computation and sampling library) - -# Copyright (c) 2012-2020 Vissarion Fisikopoulos -# Copyright (c) 2018-2020 Apostolos Chalkis -# Copyright (c) 2020-2020 Marios Papachristou - -# Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2020 program. - -# Licensed under GNU LGPL.3, see LICENCE file - -# Example script for using the logconcave sampling methods - -# Import required libraries -library(ggplot2) -library(volesti) - -# Sampling from logconcave density example - -# Helper function -norm_vec <- function(x) sqrt(sum(x^2)) - -# Negative log-probability oracle -f <- function(x) (norm_vec(x)^2 + sum(x)) - -# Negative log-probability gradient oracle -grad_f <- function(x) (2 * x + 1) - -dimension <- 50 -facets <- 200 - -# Create domain of truncation -H <- gen_rand_hpoly(dimension, facets, seed = 15) - -# Rounding -Tr <- rounding(H, seed = 127) - -P <- Hpolytope$new(A = Tr$Mat[1:nrow(Tr$Mat), 2:ncol(Tr$Mat)], b = Tr$Mat[,1]) - -x_min = matrix(0, dimension, 1) - -# Warm start point from truncated Gaussian -warm_start <- sample_points(P, n = 1, random_walk = list("nburns" = 5000), distribution = list("density" = "gaussian", "variance" = 1/2, "mode" = x_min)) - -# Sample points -n_samples <- 20000 - -samples <- sample_points(P, n = n_samples, random_walk = list("walk" = "NUTS", "solver" = "leapfrog", "starting_point" = warm_start[,1]), - distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f)) - -# Plot histogram -hist(samples[1,], probability=TRUE, breaks = 100) - -psrfs <- psrf_univariate(samples) -n_ess <- ess(samples) - diff --git a/R-proj/examples/logconcave/simple_crhmc.R b/R-proj/examples/logconcave/simple_crhmc.R deleted file mode 100644 index 5fee2b8b8..000000000 --- a/R-proj/examples/logconcave/simple_crhmc.R +++ /dev/null @@ -1,49 +0,0 @@ -# VolEsti (volume computation and sampling library) - -# Copyright (c) 2012-2020 Vissarion Fisikopoulos -# Copyright (c) 2018-2020 Apostolos Chalkis -# Copyright (c) 2020-2020 Marios Papachristou -# Copyright (c) 2022-2022 Ioannis Iakovidis - -# Contributed and/or modified by Ioannis Iakovidis, as part of Google Summer of Code 2022 program. - -# Licensed under GNU LGPL.3, see LICENCE file - -# Example script for using the logconcave sampling methods - -# Import required libraries -library(volesti) - -# Sampling from uniform density example - -logconcave_sample<- function(P,distribution, n_samples ,n_burns){ - if (distribution == "uniform"){ - f <- function(x) (0) - grad_f <- function(x) (0) - L=1 - m=1 - pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f, "L_" = L, "m" = m)) - return(max(psrf_univariate(pts, "interval"))) - } - else if(distribution == "gaussian"){ - pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "variance"=8)) - return(max(psrf_univariate(pts, "interval"))) - } -} - -for (i in 1:2) { - - if (i==1) { - distribution = 'gaussian' - cat("Gaussian ") - } else { - distribution = 'uniform' - cat("Uniform ") - } - - P = gen_simplex(10, 'H') - psrf = logconcave_sample(P,distribution,5000,2000) - cat("psrf = ") - cat(psrf) - cat("\n") -} diff --git a/R-proj/examples/logconcave/simple_hmc.R b/R-proj/examples/logconcave/simple_hmc.R deleted file mode 100644 index 860677468..000000000 --- a/R-proj/examples/logconcave/simple_hmc.R +++ /dev/null @@ -1,61 +0,0 @@ -# VolEsti (volume computation and sampling library) - -# Copyright (c) 2012-2020 Vissarion Fisikopoulos -# Copyright (c) 2018-2020 Apostolos Chalkis -# Copyright (c) 2020-2020 Marios Papachristou - -# Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2020 program. - -# Licensed under GNU LGPL.3, see LICENCE file - -# Example script for using the logconcave sampling methods - -# Import required libraries -library(ggplot2) -library(volesti) - -# Sampling from logconcave density example - -# Helper function -norm_vec <- function(x) sqrt(sum(x^2)) - -# Negative log-probability oracle -f <- function(x) (norm_vec(x)^2 + sum(x)) - -# Negative log-probability gradient oracle -grad_f <- function(x) (2 * x + 1) - -# Interval [-1, 1] -A = matrix(c(1, -1), ncol=1, nrow=2, byrow=TRUE) -b = c(2,1) - -# Create domain of truncation -P <- volesti::Hpolytope$new(A, b) - -# Mode of logconcave density -x_min <- c(-0.5) - -# Smoothness and strong-convexity -L <- 2 -m <- 2 - -# Warm start point from truncated Gaussian -warm_start <- sample_points(P, n = 1, random_walk = list("nburns" = 5000), distribution = list("density" = "gaussian", "variance" = 1/L, "mode" = x_min)) - -# Sample points -n_samples <- 20000 -n_burns <- n_samples / 2 - -pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "HMC", "step_size" = 0.3, "nburns" = n_burns, "walk_length" = 3, "solver" = "leapfrog", "starting_point" = warm_start[,1]), distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f, "L_" = L, "m" = m)) -# pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "HMC", "step_size" = 0.3, "nburns" = n_burns, "walk_length" = 3, "solver" = "leapfrog", "starting_point" = warm_start[,1]), distribution = list("density" = "logconcave", "mode" = x_min, "variance" = 1)) - -# Plot histogram -hist(pts, probability=TRUE, breaks = 100) - -cat("Sample mean is: ") -sample_mean <- mean(pts) -cat(sample_mean) -cat("\n") -cat("Sample variance is: ") -sample_variance <- mean((pts - sample_mean)^2) -cat(sample_variance) diff --git a/R-proj/examples/logconcave/simple_hmc_rand_poly.R b/R-proj/examples/logconcave/simple_hmc_rand_poly.R deleted file mode 100644 index b352c9a16..000000000 --- a/R-proj/examples/logconcave/simple_hmc_rand_poly.R +++ /dev/null @@ -1,58 +0,0 @@ -# VolEsti (volume computation and sampling library) - -# Copyright (c) 2012-2020 Vissarion Fisikopoulos -# Copyright (c) 2018-2020 Apostolos Chalkis -# Copyright (c) 2020-2020 Marios Papachristou - -# Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2020 program. - -# Licensed under GNU LGPL.3, see LICENCE file - -# Example script for using the logconcave sampling methods - -# Import required libraries -library(ggplot2) -library(volesti) - -# Sampling from logconcave density example - -# Helper function -norm_vec <- function(x) sqrt(sum(x^2)) - -# Negative log-probability oracle -f <- function(x) (norm_vec(x)^2 + sum(x)) - -# Negative log-probability gradient oracle -grad_f <- function(x) (2 * x + 1) - -dimension <- 50 -facets <- 200 - -# Create domain of truncation -H <- gen_rand_hpoly(dimension, facets, seed = 15) - -# Rounding -Tr <- rounding(H, seed = 127) - -P <- Hpolytope$new(A = Tr$Mat[1:nrow(Tr$Mat), 2:ncol(Tr$Mat)], b = Tr$Mat[,1]) - -x_min = matrix(0, dimension, 1) - -# Smoothness and strong-convexity -L <- 2 -m <- 2 - -# Warm start point from truncated Gaussian -warm_start <- sample_points(P, n = 1, random_walk = list("nburns" = 5000), distribution = list("density" = "gaussian", "variance" = 1/L, "mode" = x_min)) - -# Sample points -n_samples <- 20000 -n_burns <- n_samples / 2 - -samples <- sample_points(P, n = n_samples, random_walk = list("walk" = "HMC", "step_size" = 0.03, "nburns" = n_burns, "walk_length" = 3, "solver" = "leapfrog", "starting_point" = warm_start[,1]), distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f, "L_" = L, "m" = m)) - -# Plot histogram -hist(samples[1,], probability=TRUE, breaks = 100) - -psrfs <- psrf_univariate(samples) -n_ess <- ess(samples) \ No newline at end of file diff --git a/R-proj/examples/logconcave/simple_ode.R b/R-proj/examples/logconcave/simple_ode.R deleted file mode 100644 index 10d0c6d88..000000000 --- a/R-proj/examples/logconcave/simple_ode.R +++ /dev/null @@ -1,31 +0,0 @@ -# VolEsti (volume computation and sampling library) - -# Copyright (c) 2012-2020 Vissarion Fisikopoulos -# Copyright (c) 2018-2020 Apostolos Chalkis -# Copyright (c) 2020-2020 Marios Papachristou - -# Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2020 program. - -# Licensed under GNU LGPL.3, see LICENCE file - -# Example script for ODE solvers -library(volesti) - -F <- function (x) (-x) -order <- 2 -step_size <- 0.01 -n <- 1000 -initial_conditions <- list("x_1" = c(0), "x_2" = c(1)) -initial_time <- 0 - -# Do not impose constraints -domains <- list() - -# Call the ode solver -states <- volesti::ode_solve(dimension=1, n=n, F=F, initial_time=initial_time, step_size=step_size, order=order, method="leapfrog", initial_conditions=initial_conditions, domains = list()) - -x <- states[["x_1"]] -v <- states[["x_2"]] - -plot(x, v) - diff --git a/R-proj/examples/logconcave/simple_ode_truncated.R b/R-proj/examples/logconcave/simple_ode_truncated.R deleted file mode 100644 index be2706c46..000000000 --- a/R-proj/examples/logconcave/simple_ode_truncated.R +++ /dev/null @@ -1,34 +0,0 @@ -# VolEsti (volume computation and sampling library) - -# Copyright (c) 2012-2020 Vissarion Fisikopoulos -# Copyright (c) 2018-2020 Apostolos Chalkis -# Copyright (c) 2020-2020 Marios Papachristou - -# Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2020 program. - -# Licensed under GNU LGPL.3, see LICENCE file - -# Example script for truncated ODE solvers -library(volesti) - -F <- function (x) (x) -order <- 1 -step_size <- 0.01 -n <- 1000 -initial_conditions <- list("x_1" = c(0.1)) -initial_time <- 0 - -A <- matrix(c(1, -1), ncol=1, nrow=2, byrow=TRUE) -b <- c(1, 0) - -# Create domain of truncation -P_1 <- volesti::Hpolytope$new(A, b) -domains <- list("P_1" = P_1) - -# Call the ode solver -states <- volesti::ode_solve(dimension=1, n=n, F=F, initial_time=initial_time, step_size=step_size, order=order, method="euler", initial_conditions=initial_conditions, domains = domains) - -x <- states[["x_1"]] -t <- step_size * seq(0, n - 1) - -plot(t, x) diff --git a/R-proj/examples/logconcave/sparse_crhmc.R b/R-proj/examples/logconcave/sparse_crhmc.R deleted file mode 100644 index 373371986..000000000 --- a/R-proj/examples/logconcave/sparse_crhmc.R +++ /dev/null @@ -1,98 +0,0 @@ -# VolEsti (volume computation and sampling library) - -# Copyright (c) 2012-2020 Vissarion Fisikopoulos -# Copyright (c) 2018-2020 Apostolos Chalkis -# Copyright (c) 2020-2020 Marios Papachristou -# Copyright (c) 2022-2022 Ioannis Iakovidis - -# Contributed and/or modified by Ioannis Iakovidis, as part of Google Summer of Code 2022 program. - -# Licensed under GNU LGPL.3, see LICENCE file - -# Example script for using the logconcave sampling methods - -# Import required libraries -library(ggplot2) -library(volesti) - -# Sampling from logconcave density example - -# Helper function -norm_vec <- function(x) sqrt(sum(x^2)) - -# Negative log-probability oracle -f <- function(x) (norm_vec(x)^2 + sum(x)) - -# Negative log-probability gradient oracle -grad_f <- function(x) (2 * x + 1) - -# Interval [-1, 1] -A = matrix(c(1, -1), ncol=1, nrow=2, byrow=TRUE) -b = c(2,1) - -# Create domain of truncation -P <- volesti::Hpolytope$new(A, b) - -# Mode of logconcave density -x_min <- c(-0.5) - -# Smoothness and strong-convexity -L <- 2 -m <- 2 - -# Sample points -n_samples <- 80000 -n_burns <- n_samples / 2 -cat("---Sampling without hessian\n") -pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "step_size" = 0.3, "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f, "L_" = L, "m" = m)) -jpeg("histogram_without_hessian.jpg") -# Plot histogram -hist(pts, probability=TRUE, breaks = 100) - -cat("Sample mean is: ") -sample_mean <- mean(pts) -cat(sample_mean) -cat("\n") -cat("Sample variance is: ") -sample_variance <- mean((pts - sample_mean)^2) -cat(sample_variance) -cat("\n") -invisible(capture.output(dev.off())) - -# Negative log-probability hessian oracle -hess_f <- function(x) (2) -cat("---Sampling with hessian\n") -pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "step_size" = 0.3, "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f,"negative_logprob_hessian" = hess_f, "L_" = L, "m" = m)) -jpeg("histogram_with_hessian.jpg") -# Plot histogram -hist(pts, probability=TRUE, breaks = 100) - -cat("Sample mean is: ") -sample_mean <- mean(pts) -cat(sample_mean) -cat("\n") -cat("Sample variance is: ") -sample_variance <- mean((pts - sample_mean)^2) -cat(sample_variance) -cat("\n") -invisible(capture.output(dev.off())) - -walk="CRHMC" -library(Matrix) -bineq=matrix(c(10,10,10,10,10), nrow=5, ncol=1, byrow=TRUE) -Aineq = matrix(c(1,0,-0.25,-1,2.5,1,0.4,-1,-0.9,0.5), nrow=5, ncol=2, byrow = TRUE) -Aineq = as( Aineq, 'dgCMatrix' ) -beq=matrix(,nrow=0, ncol=1, byrow=TRUE) -Aeq = matrix(, nrow=0, ncol=2, byrow = TRUE) -Aeq=as( Aeq, 'dgCMatrix' ) -lb=-100000*c(1,1); -ub=100000*c(1,1); -cat("---Sampling the normal distribution in a pentagon\n") -P <- volesti::sparse_constraint_problem$new(Aineq, bineq,Aeq, beq, lb, ub) -points <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "step_size" = 0.3, "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "variance" = 8)) -jpeg("pentagon.jpg") -plot(ggplot(data.frame( x=points[1,], y=points[2,] )) + -geom_point( aes(x=x, y=y, color=walk)) + coord_fixed(xlim = c(-15,15), -ylim = c(-15,15)) + ggtitle(sprintf("Sampling a random pentagon with walk %s", walk))) -invisible(capture.output(dev.off())) -write.table(points, file="pentagon.txt", row.names=FALSE, col.names=FALSE) diff --git a/R-proj/inst/AUTHORS b/R-proj/inst/AUTHORS deleted file mode 100644 index 956eb3255..000000000 --- a/R-proj/inst/AUTHORS +++ /dev/null @@ -1,4 +0,0 @@ -1. Bojan Nikolic . We have modified the implementations of Khachiyan's Algorithm by B. Nikolic from bnmin1-1.11 package for the Computation of Minimum Volume Enclosing Ellipsoids in /src/external/minimum_ellipsoid. - -2. Kjell Konis , Stefan I. Larimore and Timothy A. Davis , Kjell Eikland, Michel Berkelaar, Richard Stallman, Authors of lpsolve package , in /src/external/lpsolve. - diff --git a/R-proj/inst/COPYRIGHTS b/R-proj/inst/COPYRIGHTS deleted file mode 100644 index 39cabd5a7..000000000 --- a/R-proj/inst/COPYRIGHTS +++ /dev/null @@ -1,6 +0,0 @@ -All files in src/external are taken from -(i) lpsolve (https://cran.r-project.org/src/contrib/Archive/lpSolveAPI) version 5.5.2.0, -(ii) bnmin1 (https://www.mrao.cam.ac.uk/~bn204/oof/bnmin1.html) version 1.11 - -Copyrights and modification details are explicitly described at the beginning of each of those files. - diff --git a/R-proj/inst/checks/README.md b/R-proj/inst/checks/README.md deleted file mode 100644 index a21b374b9..000000000 --- a/R-proj/inst/checks/README.md +++ /dev/null @@ -1,31 +0,0 @@ -| Check | cran-name | ERRORS | WARNINGS | NOTES | URL | -| ----------------------- |-------------|:-------------:| :-------: | :---: | -- | -| `macOS 10.9 Mavericks, R-oldrel (experimental)` | `r-oldrel-osx-x86_64` | 0 | 0 | 0 | [PREPERROR](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-4561558b7b96f9d9ab4662b565f1b871)* | -| `macOS 10.11 El Capitan, R-release (experimental)` | `r-release-osx-x86_64` | 0 | 0 | 0 | [OK](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-b02c328852acddb3fcd3048f9f327055) | -| `Windows Server 2008 R2 SP1, R-oldrel, 32/64 bit` | `r-oldrel-windows-ix86+x86_64` | 0 | 0 | 0 | [OK](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-f43945e177bd0a39f2a4effa8f6e9c88) | -| `Windows Server 2008 R2 SP1, R-release, 32/64 bit` | `r-release-windows-ix86+x86_64` | 0 | 0 | 0 | [OK](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-707ffbeaa5fb7f3b00a1cff357e63c7f) | -| `Windows Server 2008 R2 SP1, R-patched, 32/64 bit` | `N/A` | 0 | 0 | 0 | [OK](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-3376f9b5f6a17ba0a2c30b0cbf0bf917) | -| `Windows Server 2008 R2 SP1, R-devel, 32/64 bit` | `r-devel-windows-ix86+x86_64` | 0 | 0 | 0 | [OK](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-901e108dfc1802c6f8726f162c95e597) | -| `Windows Server 2012, R-devel, Rtools4.0, 32/64 bit (experimental)` | `N/A` | 0 | 0 | 1 | [NOTE](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-c48dc0fa6839ef8b509c73c48f0ca887) | -| `Oracle Solaris 10, x86, 32 bit, R-patched (experimental)` | `r-patched-solaris-x86` | 0 | 0 | 0 | [OK](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-39bda3d243591a660eb3a4d2cdb4ac3f) | -| `Debian Linux, R-devel, GCC` | `r-devel-linux-x86_64-debian-gcc` | 0 | 0 | 1 | [NOTE](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-907c4d873965c64d435bc672335063eb) | -| `Debian Linux, R-devel, GCC, no long double` | `N/A` | 0 | 0 | 1 | [NOTE](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-cb5a82d9e3112bb706e6c767e778e3a5) | -| `Debian Linux, R-release, GCC` | `r-release-linux-x86_64` | 0 | 0 | 1 | [NOTE](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-4c142b0323d2f174d53a761564e0b498) | -| `Debian Linux, R-patched, GCC` | `r-patched-linux-x86_64` | 0 | 0 | 1 | [NOTE](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-031047558bf41664b387180f558adfd1) | -| `Debian Linux, R-devel, clang, ISO-8859-15 locale` | `r-devel-linux-x86_64-debian-clang` | 0 | 0 | 1 | [NOTE](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-eecaf29af2fd88fdf148667c5463e141) | -| `Fedora Linux, R-devel, GCC` | `r-devel-linux-x86_64-fedora-gcc` | 0 | 0 | 1 | [NOTE](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-f919c24153e9afd09ef0654d4799fb12) | -| `Fedora Linux, R-devel, clang, gfortran` | `r-devel-linux-x86_64-fedora-clang` | 0 | 0 | 1 | [NOTE](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-0efbc2539568b86e2fa716db1a1231d6) | -| `Ubuntu Linux 16.04 LTS, R-devel, GCC` | `N/A` | 0 | 0 | 1 | [NOTE](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-7d47fbd13a90cb4c3712232cfa320fc3) | -| `Ubuntu Linux 16.04 LTS, R-release, GCC` |`N/A` | 0 | 0 | 1 | [NOTE](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-6911a48496c075be151e7ba5d6326fbb) | -| `CentOS 6, stock R from EPEL` |`N/A` | 1 | 0 | 0 | [ERROR](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-6f5d27f5b548d46da8fcafab677b86e4)** | -| `CentOS 6 with Redhat Developer Toolset, R from EPEL` | `N/A` | 0 | 0 | 1 | [NOTE](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-efce07b30b0a73c6c4f39db5fed70efa) | -| `Debian Linux, R-devel, GCC ASAN/UBSAN` *** | `N/A` | | | | [SUCCESS](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-1d695055bc3e2facdc252827c5d27f1b) | -| `Ubuntu Linux 16.04 LTS, R-devel with rchk` *** | `N/A` | | | | [SUCCESS](https://builder.r-hub.io/status/volesti_1.0.2.tar.gz-a35e57a5f29f45f2e74e07fd3b72c9e5) | - -All the checks above have performed via [rhub](https://cran.r-project.org/web/packages/rhub/index.html) package. For more details on system characteristics see [rhub's vignettes](https://cran.r-project.org/web/packages/rhub/vignettes/rhub.html). **cran-name** column shows the correspondance to [CRAN package check flavors](https://cran.r-project.org/web/checks/check_flavors.html) - -*The check on `macOS 10.9 Mavericks, R-oldrel (experimental)` fails because the system fails to install `RcppEigen`. You can see our [issue](https://github.com/r-hub/rhub/issues/280) at [rhub's github page](https://github.com/r-hub/rhub). - -** This is expected since our C++ code needs C++11 standard at least while this systems uses gcc4.4.X which does not support C++11 - -*** Checks for compiled code diff --git a/R-proj/inst/extdata/birk4.ine b/R-proj/inst/extdata/birk4.ine deleted file mode 100644 index f7515b94d..000000000 --- a/R-proj/inst/extdata/birk4.ine +++ /dev/null @@ -1,22 +0,0 @@ -birk4.ine -H-representation -begin - 16 10 integer --2 1 1 1 1 1 1 1 1 1 - 1 -1 0 0 -1 0 0 -1 0 0 - 1 0 -1 0 0 -1 0 0 -1 0 - 1 0 0 -1 0 0 -1 0 0 -1 - 1 -1 -1 -1 0 0 0 0 0 0 - 1 0 0 0 -1 -1 -1 0 0 0 - 1 0 0 0 0 0 0 -1 -1 -1 - 0 1 0 0 0 0 0 0 0 0 - 0 0 1 0 0 0 0 0 0 0 - 0 0 0 1 0 0 0 0 0 0 - 0 0 0 0 1 0 0 0 0 0 - 0 0 0 0 0 1 0 0 0 0 - 0 0 0 0 0 0 1 0 0 0 - 0 0 0 0 0 0 0 1 0 0 - 0 0 0 0 0 0 0 0 1 0 - 0 0 0 0 0 0 0 0 0 1 -end -input_incidence diff --git a/R-proj/inst/extdata/birk5.ine b/R-proj/inst/extdata/birk5.ine deleted file mode 100644 index 7a59b3f11..000000000 --- a/R-proj/inst/extdata/birk5.ine +++ /dev/null @@ -1,31 +0,0 @@ -birk5.ine -H-representation -begin - 25 17 integer --3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -1 -1 0 0 0 -1 0 0 0 -1 0 0 0 -1 0 0 0 -1 0 -1 0 0 0 -1 0 0 0 -1 0 0 0 -1 0 0 -1 0 0 -1 0 0 0 -1 0 0 0 -1 0 0 0 -1 0 -1 0 0 0 -1 0 0 0 -1 0 0 0 -1 0 0 0 -1 -1 -1 -1 -1 -1 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 -1 -1 -1 -1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 -1 -1 -1 -1 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 -1 -1 -1 -1 -0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 -end -input_incidence diff --git a/R-proj/inst/extdata/cross_10.ext b/R-proj/inst/extdata/cross_10.ext deleted file mode 100644 index c14ee63c5..000000000 --- a/R-proj/inst/extdata/cross_10.ext +++ /dev/null @@ -1,27 +0,0 @@ -cross_10.ext -V-representation -begin - 20 11 integer - 1 0 0 0 0 0 0 0 0 0 1 - 1 1 0 0 0 0 0 0 0 0 0 - 1 0 1 0 0 0 0 0 0 0 0 - 1 0 0 1 0 0 0 0 0 0 0 - 1 0 0 0 1 0 0 0 0 0 0 - 1 0 0 0 0 1 0 0 0 0 0 - 1 0 0 0 0 0 1 0 0 0 0 - 1 0 0 0 0 0 0 1 0 0 0 - 1 0 0 0 0 0 0 0 1 0 0 - 1 0 0 0 0 0 0 0 0 1 0 - 1 0 0 0 0 0 0 0 0 0 -1 - 1 0 0 0 0 0 0 0 0 -1 0 - 1 0 0 0 0 0 0 0 -1 0 0 - 1 0 0 0 0 0 0 -1 0 0 0 - 1 0 0 0 0 0 -1 0 0 0 0 - 1 0 0 0 0 -1 0 0 0 0 0 - 1 0 0 0 -1 0 0 0 0 0 0 - 1 0 0 -1 0 0 0 0 0 0 0 - 1 0 -1 0 0 0 0 0 0 0 0 - 1 -1 0 0 0 0 0 0 0 0 0 -end -hull -incidence diff --git a/R-proj/inst/extdata/cross_10.ine b/R-proj/inst/extdata/cross_10.ine deleted file mode 100644 index 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-1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -end -hull -incidence diff --git a/R-proj/inst/extdata/linear_extensions/simple.txt b/R-proj/inst/extdata/linear_extensions/simple.txt deleted file mode 100644 index b50a96df4..000000000 --- a/R-proj/inst/extdata/linear_extensions/simple.txt +++ /dev/null @@ -1,2 +0,0 @@ -50 20 -[[1,2],[1,3],[1,5],[1,7],[2,3],[2,5],[2,8],[3,4],[3,9],[4,5],[4,7],[4,10],[5,6],[5,8],[6,7],[6,8],[6,9],[7,8],[7,9],[9,10]] diff --git a/R-proj/inst/extdata/linear_extensions/simple_poset.txt b/R-proj/inst/extdata/linear_extensions/simple_poset.txt deleted file mode 100644 index 8998e738c..000000000 --- a/R-proj/inst/extdata/linear_extensions/simple_poset.txt +++ /dev/null @@ -1,2 +0,0 @@ -4 3 -[[1,2],[1,3],[3,4]] diff --git a/R-proj/inst/extdata/prod_simplex_10_10.ine b/R-proj/inst/extdata/prod_simplex_10_10.ine deleted file mode 100644 index 0eca1b7d1..000000000 --- a/R-proj/inst/extdata/prod_simplex_10_10.ine +++ /dev/null @@ -1,28 +0,0 @@ -prod_simplex_10.ine -H-representation -begin - 21 21 integer -0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 0 0 0 0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 0 0 0 0 0 0 0 0 0 0 -end -input_incidence diff --git a/R-proj/inst/extdata/sdpa_n2m3.txt b/R-proj/inst/extdata/sdpa_n2m3.txt deleted file mode 100644 index 0459374cf..000000000 --- a/R-proj/inst/extdata/sdpa_n2m3.txt +++ /dev/null @@ -1,13 +0,0 @@ -2 -1 -3 -1 1 --1 0 0 - 0 -2 1 - 0 1 -2 - 1 -0 -0 --0 -0 -1 --0 -1 -0 --0 -0 1 --0 -0 -0 - 1 -0 -0 diff --git a/R-proj/inst/extdata/simplex10.ext b/R-proj/inst/extdata/simplex10.ext deleted file mode 100644 index c40976554..000000000 --- a/R-proj/inst/extdata/simplex10.ext +++ /dev/null @@ -1,18 +0,0 @@ -simplex_10.ext -V-representation -begin - 11 11 integer -1 1 0 0 0 0 0 0 0 0 0 -1 0 1 0 0 0 0 0 0 0 0 -1 0 0 1 0 0 0 0 0 0 0 -1 0 0 0 1 0 0 0 0 0 0 -1 0 0 0 0 1 0 0 0 0 0 -1 0 0 0 0 0 1 0 0 0 0 -1 0 0 0 0 0 0 1 0 0 0 -1 0 0 0 0 0 0 0 1 0 0 -1 0 0 0 0 0 0 0 0 1 0 -1 0 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 0 0 0 0 -end -hull -incidence diff --git a/R-proj/inst/extdata/simplex10.ine b/R-proj/inst/extdata/simplex10.ine deleted file mode 100644 index 0e3cb59ac..000000000 --- a/R-proj/inst/extdata/simplex10.ine +++ /dev/null @@ -1,17 +0,0 @@ -simplex_10.ine -H-representation -begin - 11 11 integer -0 1 0 0 0 0 0 0 0 0 0 -0 0 1 0 0 0 0 0 0 0 0 -0 0 0 1 0 0 0 0 0 0 0 -0 0 0 0 1 0 0 0 0 0 0 -0 0 0 0 0 1 0 0 0 0 0 -0 0 0 0 0 0 1 0 0 0 0 -0 0 0 0 0 0 0 1 0 0 0 -0 0 0 0 0 0 0 0 1 0 0 -0 0 0 0 0 0 0 0 0 1 0 -0 0 0 0 0 0 0 0 0 0 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -end -input_incidence diff --git a/R-proj/inst/extdata/skinny_cube10.ine b/R-proj/inst/extdata/skinny_cube10.ine deleted file mode 100644 index aba4f0524..000000000 --- a/R-proj/inst/extdata/skinny_cube10.ine +++ /dev/null @@ -1,26 +0,0 @@ -cube_10.ine -H-representation -begin - 20 11 integer - 100 1 0 0 0 0 0 0 0 0 0 - 1 0 1 0 0 0 0 0 0 0 0 - 1 0 0 1 0 0 0 0 0 0 0 - 1 0 0 0 1 0 0 0 0 0 0 - 1 0 0 0 0 1 0 0 0 0 0 - 1 0 0 0 0 0 1 0 0 0 0 - 1 0 0 0 0 0 0 1 0 0 0 - 1 0 0 0 0 0 0 0 1 0 0 - 1 0 0 0 0 0 0 0 0 1 0 - 1 0 0 0 0 0 0 0 0 0 1 - 100 -1 0 0 0 0 0 0 0 0 0 - 1 0 -1 0 0 0 0 0 0 0 0 - 1 0 0 -1 0 0 0 0 0 0 0 - 1 0 0 0 -1 0 0 0 0 0 0 - 1 0 0 0 0 -1 0 0 0 0 0 - 1 0 0 0 0 0 -1 0 0 0 0 - 1 0 0 0 0 0 0 -1 0 0 0 - 1 0 0 0 0 0 0 0 -1 0 0 - 1 0 0 0 0 0 0 0 0 -1 0 - 1 0 0 0 0 0 0 0 0 0 -1 -end -input_incidence diff --git a/R-proj/man/Hpolytope.Rd b/R-proj/man/Hpolytope.Rd deleted file mode 100644 index c0b826747..000000000 --- a/R-proj/man/Hpolytope.Rd +++ /dev/null @@ -1,19 +0,0 @@ -\name{Hpolytope} -\alias{Hpolytope} -\title{An \code{R} class to represent H-polytopes.} - -\description{ -A H-polytope is a convex polytope defined by a set of linear inequalities or equivalently a \eqn{d}-dimensional H-polytope with \eqn{m} facets is defined by a \eqn{m\times d} matrix A and a \eqn{m}-dimensional vector b, s.t.: \eqn{P=\{x\ |\ Ax\leq b\} }. -} -\section{Fields}{ -\itemize{ -\item{\code{A} }{ A \eqn{m\times d} numerical matrix A} - -\item{\code{b} }{ \eqn{m}-dimensional vector b} - -\item{\code{type} }{ An integer that declares the representation of the polytope. For H-representation the default value is 1.} - -\item{\code{dimension} }{ The dimension of the polytope.} - -\item{\code{volume} }{ The volume of the polytope, if it is known.} -}} diff --git a/R-proj/man/Rcpp_Hpolytope.Rd b/R-proj/man/Rcpp_Hpolytope.Rd deleted file mode 100644 index 88fa5b766..000000000 --- a/R-proj/man/Rcpp_Hpolytope.Rd +++ /dev/null @@ -1,28 +0,0 @@ -\docType{class} -\name{Rcpp_Hpolytope} -\alias{Rcpp_Hpolytope-class} -\alias{[,Rcpp_Hpolytope-method} -\alias{[,Rcpp_Hpolytope,ANY,ANY,ANY-method} -\alias{$<-,Rcpp_Hpolytope-method} -\alias{$,Rcpp_Hpolytope-method} -\alias{filepaths<-,Rcpp_Hpolytope-method} -\title{ -An \code{Rcpp} class to represent H-polytopes, exposed to \code{R} via modules. -} -\description{ -A H-polytope is a convex polytope defined by a set of linear inequalities or equivalently a \eqn{d}-dimensional H-polytope with \eqn{m} facets is defined by a \eqn{m\times d} matrix A and a \eqn{m}-dimensional vector b, s.t.: \eqn{P=\{ Ax\leq b \} }. -} -\details{ -\describe{ -\item{\code{A} }{ A \eqn{m\times d} numerical matrix A} - -\item{\code{b} }{ \eqn{m}-dimensional vector b} - -\item{\code{type} }{ An integer that declares the representation of the polytope. For H-representation the default value is 1.} - -\item{\code{dimension} }{ The dimension of the polytope.} - -\item{\code{volume} }{ The volume of the polytope, if it is known.} - } -} -\keyword{internal} diff --git a/R-proj/man/Rcpp_Spectrahedron.Rd b/R-proj/man/Rcpp_Spectrahedron.Rd deleted file mode 100644 index 3ac432e59..000000000 --- a/R-proj/man/Rcpp_Spectrahedron.Rd +++ /dev/null @@ -1,21 +0,0 @@ -\docType{class} -\name{Rcpp_Spectrahedron} -\alias{Rcpp_Spectrahedron-class} -\alias{[,Rcpp_Spectrahedron-method} -\alias{[,Rcpp_Spectrahedron,ANY,ANY,ANY-method} -\alias{$<-,Rcpp_Spectrahedron-method} -\alias{$,Rcpp_Spectrahedron-method} -\alias{filepaths<-,Rcpp_Spectrahedron-method} -\title{ -An \code{Rcpp} class to represent spectrahedra, exposed to \code{R} via modules. -} -\description{ -A spectrahedron is a convex body defined by a linear matrix inequality of the form \eqn{A_0 + x_1 A_1 + ... + x_n A_n \preceq 0}. -The matrices \eqn{A_i} are symmetric \eqn{m \times m} real matrices and \eqn{\preceq 0} denoted negative semidefiniteness. -} -\details{ -\describe{ -\item{\code{matrices} }{A list with the matrices \eqn{A_0, A_1, ..., A_n}} - } -} - diff --git a/R-proj/man/Rcpp_Vpolytope.Rd b/R-proj/man/Rcpp_Vpolytope.Rd deleted file mode 100644 index 95c46c099..000000000 --- a/R-proj/man/Rcpp_Vpolytope.Rd +++ /dev/null @@ -1,26 +0,0 @@ -\docType{class} -\name{Rcpp_Vpolytope} -\alias{Rcpp_Vpolytope-class} -\alias{[,Rcpp_Vpolytope-method} -\alias{[,Rcpp_Vpolytope,ANY,ANY,ANY-method} -\alias{$<-,Rcpp_Vpolytope-method} -\alias{$,Rcpp_Vpolytope-method} -\alias{filepaths<-,Rcpp_Vpolytope-method} -\title{ -An \code{Rcpp} class to represent V-polytopes, exposed to \code{R} via modules. -} -\description{ -A V-polytope is defined as the convex hull of \eqn{m} \eqn{d}-dimensional points which corresponds to its vertices. -} -\details{ -\describe{ - \item{\code{V} }{ A \eqn{m\times d} numerical matrix that contains the vertices row-wise} - - \item{\code{type} }{ An integer that declares the representation of the polytope. For V-representation the default value is 2.} - - \item{\code{dimension} }{ The dimension of the polytope.} - - \item{\code{volume} }{ The volume of the polytope, if it is known.} - } -} -\keyword{internal} diff --git a/R-proj/man/Rcpp_VpolytopeIntersection.Rd b/R-proj/man/Rcpp_VpolytopeIntersection.Rd deleted file mode 100644 index ec308c3be..000000000 --- a/R-proj/man/Rcpp_VpolytopeIntersection.Rd +++ /dev/null @@ -1,28 +0,0 @@ -\docType{class} -\name{Rcpp_VpolytopeIntersection} -\alias{Rcpp_VpolytopeIntersection-class} -\alias{[,Rcpp_VpolytopeIntersection-method} -\alias{[,Rcpp_VpolytopeIntersection,ANY,ANY,ANY-method} -\alias{$<-,Rcpp_VpolytopeIntersection-method} -\alias{$,Rcpp_VpolytopeIntersection-method} -\alias{filepaths<-,Rcpp_VpolytopeIntersection-method} -\title{ -An \code{Rcpp} class to represent the intersection of two V-polytope, exposed to \code{R} via modules. -} -\description{ -An intersection of two V-polytopes, \eqn{P_1}, \eqn{P_2}, is defined by the intersection of the two coresponding convex hulls. -} -\details{ -\describe{ -\item{\code{V1} }{ The numerical matrix that contains the vertices of \eqn{P_1} row-wise.} - -\item{\code{V2} }{ The numerical matrix that contains the vertices of \eqn{P_2} row-wise.} - -\item{\code{type} }{ An integer that declares the representation of the polytope. For these kinf of polytopes the default value is 4.} - -\item{\code{dimension} }{ The dimension of the polytope.} - -\item{\code{volume} }{ The volume of the polytope, if it is known.} - } -} -\keyword{internal} diff --git a/R-proj/man/Rcpp_Zonotope.Rd b/R-proj/man/Rcpp_Zonotope.Rd deleted file mode 100644 index 8c010851e..000000000 --- a/R-proj/man/Rcpp_Zonotope.Rd +++ /dev/null @@ -1,26 +0,0 @@ -\docType{class} -\name{Rcpp_Zonotope} -\alias{Rcpp_Zonotope-class} -\alias{[,Rcpp_Zonotope-method} -\alias{[,Rcpp_Zonotope,ANY,ANY,ANY-method} -\alias{$<-,Rcpp_Zonotope-method} -\alias{$,Rcpp_Zonotope-method} -\alias{filepaths<-,Rcpp_Zonotope-method} -\title{ -An \code{Rcpp} class to represent zonotopes, exposed to \code{R} via modules. -} -\description{ -A zonotope is a convex polytope defined by the Minkowski sum of \eqn{m} \eqn{d}-dimensional segments. -} -\details{ -\describe{ - \item{\code{G} }{ A \eqn{m\times d} numerical matrix that contains the segments (or generators) row-wise} - -\item{\code{type} }{ An integer that declares the representation of the polytope. For zonotopes the default value is 3.} - -\item{\code{dimension} }{ The dimension of the polytope.} - -\item{\code{volume} }{ The volume of the polytope, if it is known.} - } -} -\keyword{internal} diff --git a/R-proj/man/Rcpp_sparse_constraint_problem.Rd b/R-proj/man/Rcpp_sparse_constraint_problem.Rd deleted file mode 100644 index 82f81f6c9..000000000 --- a/R-proj/man/Rcpp_sparse_constraint_problem.Rd +++ /dev/null @@ -1,34 +0,0 @@ -\docType{class} -\name{Rcpp_sparse_constraint_problem} -\alias{Rcpp_sparse_constraint_problem-class} -\alias{[,Rcpp_sparse_constraint_problem-method} -\alias{[,Rcpp_sparse_constraint_problem,ANY,ANY,ANY-method} -\alias{$<-,Rcpp_sparse_constraint_problem-method} -\alias{$,Rcpp_sparse_constraint_problem-method} -\alias{filepaths<-,Rcpp_sparse_constraint_problem-method} -\title{ -An \code{Rcpp} class to represent sparse_constraint_problems, exposed to \code{R} via modules. -} -\description{ -A constraint problem is defined by a set of linear inequalities and equalities or equivalently a \eqn{d}-dimensional constraint problem is defined by a \eqn{mineq\times d} matrix Aineq and a \eqn{mineq}-dimensional vector bineq, s.t.: \eqn{Aineqx\leq bineq}, a \eqn{meq\times d} matrix Aeq and a \eqn{meq}-dimensional vector beq, s.t.: \eqn{Aeqx= beq} and two \eqn{d} vectors lb, ub such that \eqn{lb\leq x \leq ub}.} -\details{ -\describe{ -\item{\code{Aineq} }{\eqn{mineq\times d} sparse matrix Aineq} - -\item{\code{bineq} }{\eqn{mineq}-dimensional vector bineq} - -\item{\code{Aeq} }{\eqn{meq\times d} sparse matrix Aeq} - -\item{\code{beq} }{\eqn{meq}-dimensional vector beq} - -\item{\code{lb} }{\eqn{d}-dimensional vector bineq} - -\item{\code{ub} }{\eqn{d}-dimensional vector bineq} - -\item{\code{type} }{An integer that declares the representation of the polytope. For sparse_constraint_problem the default value is 5.} - -\item{\code{dimension} }{The dimension of the polytope.} - - } -} -\keyword{internal} diff --git a/R-proj/man/Spectrahedron.Rd b/R-proj/man/Spectrahedron.Rd deleted file mode 100644 index 6c6df1d8a..000000000 --- a/R-proj/man/Spectrahedron.Rd +++ /dev/null @@ -1,12 +0,0 @@ -\name{Spectrahedron} -\alias{Spectrahedron} -\title{An \code{R} class to represent spectrahedra.} - -\description{ -A spectrahedron is a convex body defined by a linear matrix inequality of the form \eqn{A_0 + x_1 A_1 + ... + x_n A_n \preceq 0}. -The matrices \eqn{A_i} are symmetric \eqn{m \times m} real matrices and \eqn{\preceq 0} denoted negative semidefiniteness. -} -\section{Fields}{ -\itemize{ -\item{\code{matrices} }{A list with the matrices \eqn{A_0, A_1, ..., A_n}} -}} diff --git a/R-proj/man/Vpolytope.Rd b/R-proj/man/Vpolytope.Rd deleted file mode 100644 index e330af8b3..000000000 --- a/R-proj/man/Vpolytope.Rd +++ /dev/null @@ -1,17 +0,0 @@ -\name{Vpolytope} -\alias{Vpolytope} -\title{An \code{R} class to represent V-polytopes.} - -\description{ - A V-polytope is defined as the convex hull of \eqn{m} \eqn{d}-dimensional points which corresponds to its vertices. -} -\section{Fields}{ -\itemize{ - \item{\code{V} }{ A \eqn{m\times d} numerical matrix that contains the vertices row-wise} - - \item{\code{type} }{ An integer that declares the representation of the polytope. For V-representation the default value is 2.} - - \item{\code{dimension} }{ The dimension of the polytope.} - - \item{\code{volume} }{ The volume of the polytope, if it is known.} -}} diff --git a/R-proj/man/VpolytopeIntersection.Rd b/R-proj/man/VpolytopeIntersection.Rd deleted file mode 100644 index f7a75c662..000000000 --- a/R-proj/man/VpolytopeIntersection.Rd +++ /dev/null @@ -1,19 +0,0 @@ -\name{VpolytopeIntersection} -\alias{VpolytopeIntersection} -\title{An \code{R} class to represent the intersection of two V-polytopes.} - -\description{ - An intersection of two V-polytopes, \eqn{P_1}, \eqn{P_2}, is defined by the intersection of the two corresponding convex hulls. -} -\section{Fields}{ -\itemize{ -\item{\code{V1} }{ The numerical matrix that contains the vertices of \eqn{P_1} row-wise.} - -\item{\code{V2} }{ The numerical matrix that contains the vertices of \eqn{P_2} row-wise.} - -\item{\code{type} }{ An integer that declares the representation of the polytope. For this polytope the default value is 4.} - -\item{\code{dimension} }{ The dimension of the polytope.} - -\item{\code{volume} }{ The volume of the polytope, if it is known.} -}} diff --git a/R-proj/man/Zonotope.Rd b/R-proj/man/Zonotope.Rd deleted file mode 100644 index 959fbed5d..000000000 --- a/R-proj/man/Zonotope.Rd +++ /dev/null @@ -1,17 +0,0 @@ -\name{Zonotope} -\alias{Zonotope} -\title{An \code{R} class to represent zonotopes.} - -\description{ -A zonotope is a convex polytope defined by the Minkowski sum of \eqn{m} \eqn{d}-dimensional segments. -} -\section{Fields}{ -\itemize{ -\item{\code{G} }{ A \eqn{m\times d} numerical matrix that contains the segments (or generators) row-wise} - -\item{\code{type} }{ An integer that declares the representation of the polytope. For zonotopes the default value is 3.} - -\item{\code{dimension} }{ The dimension of the polytope.} - -\item{\code{volume} }{ The volume of the polytope, if it is known.} -}} diff --git a/R-proj/man/compute_indicators.Rd b/R-proj/man/compute_indicators.Rd deleted file mode 100644 index c0eaadd9e..000000000 --- a/R-proj/man/compute_indicators.Rd +++ /dev/null @@ -1,49 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/compute_indicators.R -\name{compute_indicators} -\alias{compute_indicators} -\title{Compute an indicator for each time period that describes the state of a market.} -\usage{ -compute_indicators( - returns, - win_length = NULL, - m = NULL, - n = NULL, - nwarning = NULL, - ncrisis = NULL, - seed = NULL -) -} -\arguments{ -\item{returns}{A \eqn{d}-dimensional vector that describes the direction of the first family of parallel hyperplanes.} - -\item{win_length}{Optional. The length of the sliding window. The default value is 60.} - -\item{m}{Optional. The number of slices for the copula. The default value is 100.} - -\item{n}{Optional. The number of points to sample. The default value is \eqn{5\cdot 10^5}.} - -\item{nwarning}{Optional. The number of consecutive indicators larger than 1 required to declare a warning period. The default value is 60.} - -\item{ncrisis}{Optional. The number of consecutive indicators larger than 1 required to declare a crisis period. The default value is 100.} - -\item{seed}{Optional. A fixed seed for the number generator.} -} -\value{ -A list that contains the indicators and the corresponding vector that label each time period with respect to the market state: a) normal, b) crisis, c) warning. -} -\description{ -Given a matrix that contains row-wise the assets' returns and a sliding window \code{win_length}, this function computes an approximation of the joint distribution (copula, e.g. see \url{https://en.wikipedia.org/wiki/Copula_(probability_theory)}) between portfolios' return and volatility in each time period defined by \code{win_len}. -For each copula it computes an indicator: If the indicator is large it corresponds to a crisis period and if it is small it corresponds to a normal period. -In particular, the periods over which the indicator is greater than 1 for more than 60 consecutive sliding windows are warnings and for more than 100 are crisis. The sliding window is shifted by one day. -} -\examples{ -# simple example on random asset returns -asset_returns = replicate(10, rnorm(14)) -market_states_and_indicators = compute_indicators(asset_returns, 10, 10, 10000, 2, 3) - -} -\references{ -\cite{L. Cales, A. Chalkis, I.Z. Emiris, V. Fisikopoulos, -\dQuote{Practical volume computation of structured convex bodies, and an application to modeling portfolio dependencies and financial crises,} \emph{Proc. of Symposium on Computational Geometry, Budapest, Hungary,} 2018.} -} diff --git a/R-proj/man/copula.Rd b/R-proj/man/copula.Rd deleted file mode 100644 index be99e4892..000000000 --- a/R-proj/man/copula.Rd +++ /dev/null @@ -1,48 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{copula} -\alias{copula} -\title{Construct a copula using uniform sampling from the unit simplex} -\usage{ -copula(r1, r2 = NULL, sigma = NULL, m = NULL, n = NULL, seed = NULL) -} -\arguments{ -\item{r1}{The \eqn{d}-dimensional normal vector of the first family of parallel hyperplanes.} - -\item{r2}{Optional. The \eqn{d}-dimensional normal vector of the second family of parallel hyperplanes.} - -\item{sigma}{Optional. The \eqn{d\times d} symmetric positive semidefine matrix that describes the family of concentric ellipsoids centered at the origin.} - -\item{m}{The number of the slices for the copula. The default value is 100.} - -\item{n}{The number of points to sample. The default value is \eqn{5\cdot 10^5}.} - -\item{seed}{Optional. A fixed seed for the number generator.} -} -\value{ -A \eqn{m\times m} numerical matrix that corresponds to a copula. -} -\description{ -Given two families of parallel hyperplanes or a family of parallel hyperplanes and a family of concentric ellispoids centered at the origin intersecting the canonical simplex, this function uniformly samples from the canonical simplex and construct an approximation of the bivariate probability distribution, called copula (see \url{https://en.wikipedia.org/wiki/Copula_(probability_theory)}). -At least two families of hyperplanes or one family of hyperplanes and one family of ellipsoids have to be given as input. -} -\examples{ -# compute a copula for two random families of parallel hyperplanes -h1 = runif(n = 10, min = 1, max = 1000) -h1 = h1 / 1000 -h2=runif(n = 10, min = 1, max = 1000) -h2 = h2 / 1000 -cop = copula(r1 = h1, r2 = h2, m = 10, n = 100000) - -# compute a copula for a family of parallel hyperplanes and a family of conentric ellipsoids -h = runif(n = 10, min = 1, max = 1000) -h = h / 1000 -E = replicate(10, rnorm(20)) -E = cov(E) -cop = copula(r1 = h, sigma = E, m = 10, n = 100000) - -} -\references{ -\cite{L. Cales, A. Chalkis, I.Z. Emiris, V. Fisikopoulos, -\dQuote{Practical volume computation of structured convex bodies, and an application to modeling portfolio dependencies and financial crises,} \emph{Proc. of Symposium on Computational Geometry, Budapest, Hungary,} 2018.} -} diff --git a/R-proj/man/dinvweibull_with_loc.Rd b/R-proj/man/dinvweibull_with_loc.Rd deleted file mode 100644 index b6600f816..000000000 --- a/R-proj/man/dinvweibull_with_loc.Rd +++ /dev/null @@ -1,23 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/estimate_lipschitz_constant.R -\name{dinvweibull_with_loc} -\alias{dinvweibull_with_loc} -\title{Inverse weibull distribution PDF with location parameter} -\usage{ -dinvweibull_with_loc(x, k, lambda, theta) -} -\arguments{ -\item{x}{The argument of the PDF} - -\item{k}{The shape parameter} - -\item{lambda}{The scale parameter} - -\item{theta}{The location parameter} -} -\value{ -The value of the PDF of an Inverse Weibull distribution with parameters k, lambda, theta evaluated at x -} -\description{ -Inverse weibull distribution PDF with location parameter -} diff --git a/R-proj/man/direct_sampling.Rd b/R-proj/man/direct_sampling.Rd deleted file mode 100644 index 4609ec03d..000000000 --- a/R-proj/man/direct_sampling.Rd +++ /dev/null @@ -1,37 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{direct_sampling} -\alias{direct_sampling} -\title{Sample perfect uniformly distributed points from well known convex bodies: (a) the unit simplex, (b) the canonical simplex, (c) the boundary of a hypersphere or (d) the interior of a hypersphere.} -\usage{ -direct_sampling(body, n, seed = NULL) -} -\arguments{ -\item{body}{A list to request exact uniform sampling from special well known convex bodies through the following input parameters: -\itemize{ -\item{\code{type} }{ A string that declares the type of the body for the exact sampling: a) \code{'unit_simplex'} for the unit simplex, b) \code{'canonical_simplex'} for the canonical simplex, c) \code{'hypersphere'} for the boundary of a hypersphere centered at the origin, d) \code{'ball'} for the interior of a hypersphere centered at the origin.} -\item{\code{dimension} }{ An integer that declares the dimension when exact sampling is enabled for a simplex or a hypersphere.} -\item{\code{radius} }{ The radius of the \eqn{d}-dimensional hypersphere. The default value is \eqn{1}.} -}} - -\item{n}{The number of points that the function is going to sample.} - -\item{seed}{Optional. A fixed seed for the number generator.} -} -\value{ -A \eqn{d\times n} matrix that contains, column-wise, the sampled points from the convex polytope P. -} -\description{ -The \eqn{d}-dimensional unit simplex is the set of points \eqn{\vec{x}\in \R^d}, s.t.: \eqn{\sum_i x_i\leq 1}, \eqn{x_i\geq 0}. The \eqn{d}-dimensional canonical simplex is the set of points \eqn{\vec{x}\in \R^d}, s.t.: \eqn{\sum_i x_i = 1}, \eqn{x_i\geq 0}. -} -\examples{ -# 100 uniform points from the 2-d unit ball -points = direct_sampling(n = 100, body = list("type" = "ball", "dimension" = 2)) -} -\references{ -\cite{R.Y. Rubinstein and B. Melamed, -\dQuote{Modern simulation and modeling} \emph{ Wiley Series in Probability and Statistics,} 1998.} - -\cite{A Smith, Noah and W Tromble, Roy, -\dQuote{Sampling Uniformly from the Unit Simplex,} \emph{ Center for Language and Speech Processing Johns Hopkins University,} 2004.} -} diff --git a/R-proj/man/ess.Rd b/R-proj/man/ess.Rd deleted file mode 100644 index f9fbcbcb8..000000000 --- a/R-proj/man/ess.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{ess} -\alias{ess} -\title{Gelman-Rubin and Brooks-Gelman Potential Scale Reduction Factor (PSRF) for each marginal} -\usage{ -ess(samples) -} -\arguments{ -\item{samples}{A matrix that contans column-wise the sampled points from a geometric random walk.} -} -\value{ -A vector that contains the values of PSRF for each coordinate -} -\description{ -Gelman-Rubin and Brooks-Gelman Potential Scale Reduction Factor (PSRF) for each marginal -} -\references{ -\cite{Gelman, A. and Rubin, D. B., -\dQuote{Inference from iterative simulation using multiple sequences,} \emph{Statistical Science,} 1992.} - -\cite{Brooks, S. and Gelman, A., -\dQuote{General Methods for Monitoring Convergence of Iterative Simulations,} \emph{Journal of Computational and Graphical Statistics,} 1998.} -} diff --git a/R-proj/man/estimtate_lipschitz_constant.Rd b/R-proj/man/estimtate_lipschitz_constant.Rd deleted file mode 100644 index 5418857d2..000000000 --- a/R-proj/man/estimtate_lipschitz_constant.Rd +++ /dev/null @@ -1,25 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/estimate_lipschitz_constant.R -\name{estimtate_lipschitz_constant} -\alias{estimtate_lipschitz_constant} -\title{Estimate the Lipschitz Constant of a function f} -\usage{ -estimtate_lipschitz_constant(f, P, n) -} -\arguments{ -\item{f}{Function whose Lipschitz constant is to be estimated} - -\item{P}{Domain of f (a convex polytope)} - -\item{n}{Number of samples to take - -The procedure draws n uniform samples from P and evaluates the Lipschitz -constant at subsequent samples (where the sampler moves to a new point), -It then returns the maximum observation} -} -\value{ -An estimate of the Lipschitz constant -} -\description{ -Estimate the Lipschitz Constant of a function f -} diff --git a/R-proj/man/exact_vol.Rd b/R-proj/man/exact_vol.Rd deleted file mode 100644 index 593247409..000000000 --- a/R-proj/man/exact_vol.Rd +++ /dev/null @@ -1,38 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{exact_vol} -\alias{exact_vol} -\title{Compute the exact volume of (a) a zonotope (b) an arbitrary simplex in V-representation or (c) if the volume is known and declared by the input object.} -\usage{ -exact_vol(P) -} -\arguments{ -\item{P}{A polytope} -} -\value{ -The exact volume of the input polytope, for zonotopes, simplices in V-representation and polytopes with known exact volume -} -\description{ -Given a zonotope (as an object of class Zonotope), this function computes the sum of the absolute values of the determinants of all the \eqn{d \times d} submatrices of the \eqn{m\times d} matrix \eqn{G} that contains row-wise the \eqn{m} \eqn{d}-dimensional segments that define the zonotope. -For an arbitrary simplex that is given in V-representation this function computes the absolute value of the determinant formed by the simplex's points assuming it is shifted to the origin. -} -\examples{ - -# compute the exact volume of a 5-dimensional zonotope defined by the Minkowski sum of 10 segments -Z = gen_rand_zonotope(2, 5) -vol = exact_vol(Z) - -\donttest{# compute the exact volume of a 2-d arbitrary simplex -V = matrix(c(2,3,-1,7,0,0),ncol = 2, nrow = 3, byrow = TRUE) -P = Vpolytope$new(V) -vol = exact_vol(P) -} - -# compute the exact volume the 10-dimensional cross polytope -P = gen_cross(10,'V') -vol = exact_vol(P) -} -\references{ -\cite{E. Gover and N. Krikorian, -\dQuote{Determinants and the Volumes of Parallelotopes and Zonotopes,} \emph{Linear Algebra and its Applications, 433(1), 28 - 40,} 2010.} -} diff --git a/R-proj/man/file_to_polytope.Rd b/R-proj/man/file_to_polytope.Rd deleted file mode 100644 index 8ec843d53..000000000 --- a/R-proj/man/file_to_polytope.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/file_to_polytope.R -\name{file_to_polytope} -\alias{file_to_polytope} -\title{function to get an ine or an ext file and returns the corresponding polytope} -\usage{ -file_to_polytope(path, zonotope = FALSE) -} -\arguments{ -\item{path}{A string that containes the path to an ine or a ext file. The ine file desrcibes a H-polytope and ext file describes a V-polytope or a zonotope.} - -\item{zonotope}{A boolean parameter. It has to be TRUE when the path leads to an .ext file that describes a zonotope.} -} -\value{ -A polytope class. If the path corresponds to an ine file then the return value represents a H-polytope. If it corresponds to an ext file the return value represents a V-polytope (default choice) or a zonotope if the second argument is TRUE. -} -\description{ -For an ".ine" file it generates the corresponding H-polytope. For an ".ext" file it generates the corresponding V-polytope or zonotope. -For more details on those file formats see \url{https://github.com/GeomScale/volume_approximation/blob/develop/doc/cpp_interface.md#polytope-input}. -} diff --git a/R-proj/man/frustum_of_simplex.Rd b/R-proj/man/frustum_of_simplex.Rd deleted file mode 100644 index bcb075eb4..000000000 --- a/R-proj/man/frustum_of_simplex.Rd +++ /dev/null @@ -1,32 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{frustum_of_simplex} -\alias{frustum_of_simplex} -\title{Compute the percentage of the volume of the simplex that is contained in the intersection of a half-space and the simplex.} -\usage{ -frustum_of_simplex(a, z0) -} -\arguments{ -\item{a}{A \eqn{d}-dimensional vector that defines the direction of the hyperplane.} - -\item{z0}{The scalar that defines the half-space.} -} -\value{ -The percentage of the volume of the simplex that is contained in the intersection of a given half-space and the simplex. -} -\description{ -A half-space \eqn{H} is given as a pair of a vector \eqn{a\in R^d} and a scalar \eqn{z0\in R} s.t.: \eqn{a^Tx\leq z0}. This function calls the Ali's version of the Varsi formula to compute a frustum of the simplex. -} -\examples{ -# compute the frustum of H: -x1+x2<=0 -a=c(-1,1) -z0=0 -frustum = frustum_of_simplex(a, z0) -} -\references{ -\cite{Varsi, Giulio, -\dQuote{The multidimensional content of the frustum of the simplex,} \emph{Pacific J. Math. 46, no. 1, 303--314,} 1973.} - -\cite{Ali, Mir M., -\dQuote{Content of the frustum of a simplex,} \emph{ Pacific J. Math. 48, no. 2, 313--322,} 1973.} -} diff --git a/R-proj/man/gen_birkhoff.Rd b/R-proj/man/gen_birkhoff.Rd deleted file mode 100644 index b5659c021..000000000 --- a/R-proj/man/gen_birkhoff.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/gen_birkhoff.R -\name{gen_birkhoff} -\alias{gen_birkhoff} -\title{Generator function for Birkhoff polytope} -\usage{ -gen_birkhoff(n) -} -\arguments{ -\item{n}{The order of the Birkhoff polytope} -} -\value{ -A polytope class representing the full dimensional \eqn{n}-Birkhoff polytope in H-representation. -} -\description{ -This function can be used to generate the full dimensional \eqn{n}-Birkhoff polytope in H-representation. -The dimension of the generated polytope is \eqn{(n-1)^2}. -} -\examples{ -# generate the Birkhoff polytope of order 5 -P = gen_birkhoff(5) -} diff --git a/R-proj/man/gen_cross.Rd b/R-proj/man/gen_cross.Rd deleted file mode 100644 index 53e84c78d..000000000 --- a/R-proj/man/gen_cross.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/gen_cross.R -\name{gen_cross} -\alias{gen_cross} -\title{Generator function for cross polytopes} -\usage{ -gen_cross(dimension, representation) -} -\arguments{ -\item{dimension}{The dimension of the cross polytope.} - -\item{representation}{A string to declare the representation. It has to be \code{'H'} for H-representation or \code{'V'} for V-representation.} -} -\value{ -A polytope class representing a cross polytope in H- or V-representation. -} -\description{ -This function generates the \eqn{d}-dimensional cross polytope in H- or V-representation. -} -\examples{ -# generate a 10-dimensional cross polytope in H-representation -P = gen_cross(5, 'H') - -# generate a 15-dimension cross polytope in V-representation -P = gen_cross(15, 'V') -} diff --git a/R-proj/man/gen_cube.Rd b/R-proj/man/gen_cube.Rd deleted file mode 100644 index efb486d61..000000000 --- a/R-proj/man/gen_cube.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/gen_cube.R -\name{gen_cube} -\alias{gen_cube} -\title{Generator function for hypercubes} -\usage{ -gen_cube(dimension, representation) -} -\arguments{ -\item{dimension}{The dimension of the hypercube} - -\item{representation}{A string to declare the representation. It has to be \code{'H'} for H-representation or \code{'V'} for V-representation.} -} -\value{ -A polytope class representing the unit \eqn{d}-dimensional hypercube in H- or V-representation. -} -\description{ -This function generates the \eqn{d}-dimensional unit hypercube \eqn{[-1,1]^d} in H- or V-representation. -} -\examples{ -# generate a 10-dimensional hypercube in H-representation -P = gen_cube(10, 'H') - -# generate a 15-dimension hypercube in V-representation -P = gen_cube(5, 'V') -} diff --git a/R-proj/man/gen_prod_simplex.Rd b/R-proj/man/gen_prod_simplex.Rd deleted file mode 100644 index 108a2dc1b..000000000 --- a/R-proj/man/gen_prod_simplex.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/gen_prod_simplex.R -\name{gen_prod_simplex} -\alias{gen_prod_simplex} -\title{Generator function for product of simplices} -\usage{ -gen_prod_simplex(dimension) -} -\arguments{ -\item{dimension}{The dimension of the simplices.} -} -\value{ -A polytope class representing the product of the two \eqn{d}-dimensional unit simplices in H-representation. -} -\description{ -This function generates a \eqn{2d}-dimensional polytope that is defined as the product of two \eqn{d}-dimensional unit simplices in H-representation. -} -\examples{ -# generate a product of two 5-dimensional simplices. -P = gen_prod_simplex(5) -} diff --git a/R-proj/man/gen_rand_hpoly.Rd b/R-proj/man/gen_rand_hpoly.Rd deleted file mode 100644 index e4db1c9e5..000000000 --- a/R-proj/man/gen_rand_hpoly.Rd +++ /dev/null @@ -1,25 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/gen_rand_hpoly.R -\name{gen_rand_hpoly} -\alias{gen_rand_hpoly} -\title{Generator function for random H-polytopes} -\usage{ -gen_rand_hpoly(dimension, nfacets, seed = NULL) -} -\arguments{ -\item{dimension}{The dimension of the convex polytope.} - -\item{nfacets}{The number of the facets.} - -\item{seed}{Optional. A fixed seed for the generator.} -} -\value{ -A polytope class representing a H-polytope. -} -\description{ -This function generates a \eqn{d}-dimensional polytope in H-representation with \eqn{m} facets. We pick \eqn{m} random hyperplanes tangent on the \eqn{d}-dimensional unit hypersphere as facets. -} -\examples{ -# generate a 10-dimensional polytope with 50 facets -P = gen_rand_hpoly(10, 50) -} diff --git a/R-proj/man/gen_rand_vpoly.Rd b/R-proj/man/gen_rand_vpoly.Rd deleted file mode 100644 index e2c3c0978..000000000 --- a/R-proj/man/gen_rand_vpoly.Rd +++ /dev/null @@ -1,27 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/gen_rand_vpoly.R -\name{gen_rand_vpoly} -\alias{gen_rand_vpoly} -\title{Generator function for random V-polytopes} -\usage{ -gen_rand_vpoly(dimension, nvertices, generator = NULL, seed = NULL) -} -\arguments{ -\item{dimension}{The dimension of the convex polytope.} - -\item{nvertices}{The number of the vertices.} - -\item{generator}{The body that the generator samples uniformly the vertices from: (a) 'cube' or (b) 'sphere'.} - -\item{seed}{Optional. A fixed seed for the generator.} -} -\value{ -A polytope class representing a V-polytope. -} -\description{ -This function generates a \eqn{d}-dimensional polytope in V-representation with \eqn{m} vertices. We pick \eqn{m} random points from the boundary of the \eqn{d}-dimensional unit hypersphere as vertices. -} -\examples{ -# generate a 10-dimensional polytope defined as the convex hull of 25 random vertices -P = gen_rand_vpoly(10, 25) -} diff --git a/R-proj/man/gen_rand_zonotope.Rd b/R-proj/man/gen_rand_zonotope.Rd deleted file mode 100644 index ab971d35e..000000000 --- a/R-proj/man/gen_rand_zonotope.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/gen_rand_zonotope.R -\name{gen_rand_zonotope} -\alias{gen_rand_zonotope} -\title{Generator function for zonotopes} -\usage{ -gen_rand_zonotope(dimension, nsegments, generator = NULL, seed = NULL) -} -\arguments{ -\item{dimension}{The dimension of the zonotope.} - -\item{nsegments}{The number of segments that generate the zonotope.} - -\item{generator}{The distribution to pick the length of each segment from \eqn{[0,100]}: (a) 'uniform', (b) 'gaussian' or (c) 'exponential'.} - -\item{seed}{Optional. A fixed seed for the generator.} -} -\value{ -A polytope class representing a zonotope. -} -\description{ -This function generates a random \eqn{d}-dimensional zonotope defined by the Minkowski sum of \eqn{m} \eqn{d}-dimensional segments. -The function considers \eqn{m} random directions in \eqn{R^d}. There are three strategies to pick the length of each segment: a) it is uniformly sampled from \eqn{[0,100]}, b) it is random from \eqn{\mathcal{N}(50,(50/3)^2)} truncated to \eqn{[0,100]}, c) it is random from \eqn{Exp(1/30)} truncated to \eqn{[0,100]}. -} -\examples{ -# generate a 10-dimensional zonotope defined by the Minkowski sum of 20 segments -P = gen_rand_zonotope(10, 20) -} diff --git a/R-proj/man/gen_simplex.Rd b/R-proj/man/gen_simplex.Rd deleted file mode 100644 index bed04a3a6..000000000 --- a/R-proj/man/gen_simplex.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/gen_simplex.R -\name{gen_simplex} -\alias{gen_simplex} -\title{Generator function for simplices} -\usage{ -gen_simplex(dimension, representation) -} -\arguments{ -\item{dimension}{The dimension of the unit simplex.} - -\item{representation}{A string to declare the representation. It has to be \code{'H'} for H-representation or \code{'V'} for V-representation.} -} -\value{ -A polytope class representing the \eqn{d}-dimensional unit simplex in H- or V-representation. -} -\description{ -This function generates the \eqn{d}-dimensional unit simplex in H- or V-representation. -} -\examples{ -# generate a 10-dimensional simplex in H-representation -PolyList = gen_simplex(10, 'H') - -# generate a 20-dimensional simplex in V-representation -P = gen_simplex(20, 'V') -} diff --git a/R-proj/man/gen_skinny_cube.Rd b/R-proj/man/gen_skinny_cube.Rd deleted file mode 100644 index 916f5e20a..000000000 --- a/R-proj/man/gen_skinny_cube.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/gen_skinny_cube.R -\name{gen_skinny_cube} -\alias{gen_skinny_cube} -\title{Generator function for skinny hypercubes} -\usage{ -gen_skinny_cube(dimension) -} -\arguments{ -\item{dimension}{The dimension of the skinny hypercube.} -} -\value{ -A polytope class representing the \eqn{d}-dimensional skinny hypercube in H-representation. -} -\description{ -This function generates a \eqn{d}-dimensional skinny hypercube \eqn{[-1,1]^{d-1}\times [-100,100]}. -} -\examples{ -# generate a 10-dimensional skinny hypercube. -P = gen_skinny_cube(10) -} diff --git a/R-proj/man/geweke.Rd b/R-proj/man/geweke.Rd deleted file mode 100644 index bcaa199d8..000000000 --- a/R-proj/man/geweke.Rd +++ /dev/null @@ -1,25 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{geweke} -\alias{geweke} -\title{Geweke's MCMC diagnostic} -\usage{ -geweke(samples, frac_first = NULL, frac_last = NULL) -} -\arguments{ -\item{samples}{A matrix that contans column-wise the sampled points from a geometric random walk.} - -\item{frac_first}{Optional. The portion of the first in order points in matrix samples.} - -\item{frac_last}{Optional. The portion of the last in order points in matrix samples.} -} -\value{ -A boolean to denote if the result of Geweke diagnostic: (i) false if the null hypothesis is rejected, (ii) true if the null hypothesis is not rejected. -} -\description{ -Geweke's MCMC diagnostic -} -\references{ -\cite{Geweke, J., -\dQuote{Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments,} \emph{ In Bayesian Statistics 4. Proceedings of the Fourth Valencia International Meeting,} 1992.} -} diff --git a/R-proj/man/inner_ball.Rd b/R-proj/man/inner_ball.Rd deleted file mode 100644 index f267faad1..000000000 --- a/R-proj/man/inner_ball.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{inner_ball} -\alias{inner_ball} -\title{Compute an inscribed ball of a convex polytope} -\usage{ -inner_ball(P, lpsolve = NULL) -} -\arguments{ -\item{P}{A convex polytope. It is an object from class (a) Hpolytope or (b) Vpolytope or (c) Zonotope or (d) VpolytopeIntersection.} - -\item{lpsolve}{Optional. A boolean variable to compute the Chebychev ball of an H-polytope using the lpsolve library.} -} -\value{ -A \eqn{(d+1)}-dimensional vector that describes the inscribed ball. The first \eqn{d} coordinates corresponds to the center of the ball and the last one to the radius. -} -\description{ -For a H-polytope described by a \eqn{m\times d} matrix \eqn{A} and a \eqn{m}-dimensional vector \eqn{b}, s.t.: \eqn{P=\{x\ |\ Ax\leq b\} }, this function computes the largest inscribed ball (Chebychev ball) by solving the corresponding linear program. -For both zonotopes and V-polytopes the function computes the minimum \eqn{r} s.t.: \eqn{ r e_i \in P} for all \eqn{i=1, \dots ,d}. Then the ball centered at the origin with radius \eqn{r/ \sqrt{d}} is an inscribed ball. -} -\examples{ -# compute the Chebychev ball of the 2d unit simplex -P = gen_cube(10,'H') -ball_vec = inner_ball(P) - -# compute an inscribed ball of the 3-dimensional unit cube in V-representation -P = gen_cube(3, 'V') -ball_vec = inner_ball(P, lpsolve = TRUE) -} diff --git a/R-proj/man/loadSdpaFormatFile.Rd b/R-proj/man/loadSdpaFormatFile.Rd deleted file mode 100644 index bb6aa7558..000000000 --- a/R-proj/man/loadSdpaFormatFile.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{loadSdpaFormatFile} -\alias{loadSdpaFormatFile} -\title{Read a SDPA format file} -\usage{ -loadSdpaFormatFile(inputFile = NULL) -} -\arguments{ -\item{inputFile}{Name of the input file} -} -\value{ -A list with two named items: an item "matrices" which is a list of the matrices and an vector "objFunction" -} -\description{ -Read a SDPA format file -} -\examples{ -path = system.file('extdata', package = 'volesti') -l = loadSdpaFormatFile(paste0(path,'/sdpa_n2m3.txt')) -} diff --git a/R-proj/man/ode_solve.Rd b/R-proj/man/ode_solve.Rd deleted file mode 100644 index b4e8e4b90..000000000 --- a/R-proj/man/ode_solve.Rd +++ /dev/null @@ -1,47 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{ode_solve} -\alias{ode_solve} -\title{Solve an ODE of the form dx^n / dt^n = F(x, t)} -\usage{ -ode_solve( - n, - step_size, - order, - dimension, - initial_time, - F, - method, - domains = NULL, - initial_conditions = NULL -) -} -\arguments{ -\item{n}{The number of steps.} - -\item{step_size}{The step size.} - -\item{order}{The ODE order (default is n = 1)} - -\item{dimension}{The dimension of each derivative} - -\item{initial_time}{The initial time} - -\item{F}{The function oracle F(x, t) in the ODE.} - -\item{method}{The method to be used} - -\item{domains}{A list of n H-polytopes with keys "P_1", "P_2", ..., "P_n" that correspond to each derivative's domain} - -\item{initial_conditions}{The initial conditions provided to the solver. Must be provided in a list with keys "x_1", ..., "x_n" and column vectors as values. The state "x_n" represents the (n-1)-th order derivative with respect to time} -} -\value{ -A list which contains elements "x_1", ..., "x_n" representing each derivative results. Each "x_i" corresponds to a d x n matrix where each column represents a certain timestep of the solver. -} -\description{ -Solve an ODE of the form dx^n / dt^n = F(x, t) -} -\examples{ -# Please visit the examples directory on examples demonstrating usage of the ODE solvers. - -} diff --git a/R-proj/man/pinvweibull_with_loc.Rd b/R-proj/man/pinvweibull_with_loc.Rd deleted file mode 100644 index 08eecf30c..000000000 --- a/R-proj/man/pinvweibull_with_loc.Rd +++ /dev/null @@ -1,23 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/estimate_lipschitz_constant.R -\name{pinvweibull_with_loc} -\alias{pinvweibull_with_loc} -\title{Inverse weibull distribution CDF with location parameter} -\usage{ -pinvweibull_with_loc(q, k, lambda, theta) -} -\arguments{ -\item{q}{The argument of the CDF} - -\item{k}{The shape parameter} - -\item{lambda}{The scale parameter} - -\item{theta}{The location parameter} -} -\value{ -The value of the CDF of an Inverse Weibull distribution with parameters k, lambda, theta evaluated at q -} -\description{ -Inverse weibull distribution CDF with location parameter -} diff --git a/R-proj/man/poly_gen.Rd b/R-proj/man/poly_gen.Rd deleted file mode 100644 index be2ed4dd0..000000000 --- a/R-proj/man/poly_gen.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{poly_gen} -\alias{poly_gen} -\title{An internal Rccp function as a polytope generator} -\usage{ -poly_gen(kind_gen, Vpoly_gen, Zono_gen, dim_gen, m_gen, seed = NULL) -} -\arguments{ -\item{kind_gen}{An integer to declare the type of the polytope.} - -\item{Vpoly_gen}{A boolean parameter to declare if the requested polytope has to be in V-representation.} - -\item{Zono_gen}{A boolean parameter to declare if the requested polytope has to be a zonotope.} - -\item{dim_gen}{An integer to declare the dimension of the requested polytope.} - -\item{m_gen}{An integer to declare the number of generators for the requested random zonotope or the number of vertices for a V-polytope.} - -\item{seed}{Optional. A fixed seed for the random polytope generator.} -} -\value{ -A numerical matrix describing the requested polytope -} -\description{ -An internal Rccp function as a polytope generator -} -\keyword{internal} diff --git a/R-proj/man/psrf_multivariate.Rd b/R-proj/man/psrf_multivariate.Rd deleted file mode 100644 index b941783fa..000000000 --- a/R-proj/man/psrf_multivariate.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{psrf_multivariate} -\alias{psrf_multivariate} -\title{Gelman-Rubin Potential Scale Reduction Factor (PSRF)} -\usage{ -psrf_multivariate(samples) -} -\arguments{ -\item{samples}{A matrix that contans column-wise the sampled points from a geometric random walk.} -} -\value{ -The value of multivariate PSRF by S. Brooks and A. Gelman. -} -\description{ -Gelman-Rubin Potential Scale Reduction Factor (PSRF) -} -\references{ -\cite{Gelman, A. and Rubin, D. B., -\dQuote{Inference from iterative simulation using multiple sequences,} \emph{Statistical Science,} 1992.} - -\cite{Brooks, S. and Gelman, A., -\dQuote{General Methods for Monitoring Convergence of Iterative Simulations,} \emph{Journal of Computational and Graphical Statistics,} 1998.} -} diff --git a/R-proj/man/psrf_univariate.Rd b/R-proj/man/psrf_univariate.Rd deleted file mode 100644 index 45aff7ee0..000000000 --- a/R-proj/man/psrf_univariate.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{psrf_univariate} -\alias{psrf_univariate} -\title{Gelman-Rubin and Brooks-Gelman Potential Scale Reduction Factor (PSRF) for each marginal} -\usage{ -psrf_univariate(samples, method = NULL) -} -\arguments{ -\item{samples}{A matrix that contans column-wise the sampled points from a geometric random walk.} - -\item{method}{A string to reauest diagnostic: (i) \code{'normal'} for psrf of Gelman-Rubin and (ii) \code{'interval'} for psrf of Brooks-Gelman.} -} -\value{ -A vector that contains the values of PSRF for each coordinate -} -\description{ -Gelman-Rubin and Brooks-Gelman Potential Scale Reduction Factor (PSRF) for each marginal -} -\references{ -\cite{Gelman, A. and Rubin, D. B., -\dQuote{Inference from iterative simulation using multiple sequences,} \emph{Statistical Science,} 1992.} - -\cite{Brooks, S. and Gelman, A., -\dQuote{General Methods for Monitoring Convergence of Iterative Simulations,} \emph{Journal of Computational and Graphical Statistics,} 1998.} -} diff --git a/R-proj/man/raftery.Rd b/R-proj/man/raftery.Rd deleted file mode 100644 index 0415442e7..000000000 --- a/R-proj/man/raftery.Rd +++ /dev/null @@ -1,27 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{raftery} -\alias{raftery} -\title{Raftery and Lewis MCMC diagnostic} -\usage{ -raftery(samples, q = NULL, r = NULL, s = NULL) -} -\arguments{ -\item{samples}{A matrix that contans column-wise the sampled points from a geometric random walk.} - -\item{q}{Optional. The quantile of the quantity of interest. The default value is 0.025.} - -\item{r}{Optional. The level of precision desired. The default value is 0.01.} - -\item{s}{Optional. The probability associated with r. The default value is 0.95.} -} -\value{ -(i) The number of draws required for burn-in, (ii) the skip parameter for 1st-order Markov chain, (iii) the skip parameter sufficient to get independence chain, (iv) the number of draws required to achieve r precision, (v) the number of draws if the chain is white noise, (vi) the I-statistic from Raftery and Lewis (1992). -} -\description{ -Raftery and Lewis MCMC diagnostic -} -\references{ -\cite{Raftery, A. E. and Lewis, S. M., -\dQuote{How many iterations in the Gibbs sampler?,} \emph{Bayesian Statistics 4. Proceedings of the Fourth Valencia International Meeting,} 1992.} -} diff --git a/R-proj/man/readSdpaFormatFile.Rd b/R-proj/man/readSdpaFormatFile.Rd deleted file mode 100644 index 878a11965..000000000 --- a/R-proj/man/readSdpaFormatFile.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/read_sdpa_file.R -\name{readSdpaFormatFile} -\alias{readSdpaFormatFile} -\title{Read a SDPA format file} -\usage{ -readSdpaFormatFile(path) -} -\arguments{ -\item{path}{Name of the input file} -} -\value{ -A list with two named items: an item "matrices" which is an object of class Spectrahedron and an vector "objFunction" -} -\description{ -Read a SDPA format file and return a spectrahedron (an object of class Spectrahedron) which is defined by -the linear matrix inequality in the input file, and the objective function. -} -\examples{ -path = system.file('extdata', package = 'volesti') -l = readSdpaFormatFile(paste0(path,'/sdpa_n2m3.txt')) -Spectrahedron = l$spectrahedron -objFunction = l$objFunction -} diff --git a/R-proj/man/rotate_polytope.Rd b/R-proj/man/rotate_polytope.Rd deleted file mode 100644 index 70c9c3adf..000000000 --- a/R-proj/man/rotate_polytope.Rd +++ /dev/null @@ -1,43 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/rotate_polytope.R -\name{rotate_polytope} -\alias{rotate_polytope} -\title{Apply a random rotation to a convex polytope (H-polytope, V-polytope, zonotope or intersection of two V-polytopes)} -\usage{ -rotate_polytope(P, T = NULL, seed = NULL) -} -\arguments{ -\item{P}{A convex polytope. It is an object from class (a) Hpolytope, (b) Vpolytope, (c) Zonotope, (d) intersection of two V-polytopes.} - -\item{T}{Optional. A \eqn{d\times d} rotation matrix.} - -\item{seed}{Optional. A fixed seed for the random linear map generator.} -} -\value{ -A list that contains the rotated polytope and the matrix \eqn{T} of the linear transformation. -} -\description{ -Given a convex H- or V- polytope or a zonotope or an intersection of two V-polytopes as input, this function applies (a) a random rotation or (b) a given rotation by an input matrix \eqn{T}. -} -\details{ -Let \eqn{P} be the given polytope and \eqn{Q} the rotated one and \eqn{T} be the matrix of the linear transformation. -\itemize{ -\item{If \eqn{P} is in H-representation and \eqn{A} is the matrix that contains the normal vectors of the facets of \eqn{Q} then \eqn{AT} contains the normal vactors of the facets of \eqn{P}.} -\item{If \eqn{P} is in V-representation and \eqn{V} is the matrix that contains column-wise the vertices of \eqn{Q} then \eqn{T^TV} contains the vertices of \eqn{P}.} -\item{If \eqn{P} is a zonotope and \eqn{G} is the matrix that contains column-wise the generators of \eqn{Q} then \eqn{T^TG} contains the generators of \eqn{P}.} -\item{If \eqn{M} is a matrix that contains column-wise points in \eqn{Q} then \eqn{T^TM} contains points in \eqn{P}.} -} -} -\examples{ -# rotate a H-polytope (2d unit simplex) -P = gen_simplex(2,'H') -poly_matrix_list = rotate_polytope(P) - -# rotate a V-polytope (3d cube) -P = gen_cube(3, 'V') -poly_matrix_list = rotate_polytope(P) - -# rotate a 5-dimensional zonotope defined by the Minkowski sum of 15 segments -Z = gen_rand_zonotope(3,6) -poly_matrix_list = rotate_polytope(Z) -} diff --git a/R-proj/man/rotating.Rd b/R-proj/man/rotating.Rd deleted file mode 100644 index 4b091f815..000000000 --- a/R-proj/man/rotating.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{rotating} -\alias{rotating} -\title{An internal Rccp function for the random rotation of a convex polytope} -\usage{ -rotating(P, T = NULL, seed = NULL) -} -\arguments{ -\item{P}{A convex polytope (H-, V-polytope or a zonotope).} - -\item{T}{Optional. A rotation matrix.} - -\item{seed}{Optional. A fixed seed for the random linear map generator.} -} -\value{ -A matrix that describes the rotated polytope -} -\description{ -An internal Rccp function for the random rotation of a convex polytope -} -\keyword{internal} diff --git a/R-proj/man/round_polytope.Rd b/R-proj/man/round_polytope.Rd deleted file mode 100644 index 787e40fb0..000000000 --- a/R-proj/man/round_polytope.Rd +++ /dev/null @@ -1,47 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/round_polytope.R -\name{round_polytope} -\alias{round_polytope} -\title{Apply rounding to a convex polytope (H-polytope, V-polytope or a zonotope)} -\usage{ -round_polytope(P, method = NULL, seed = NULL) -} -\arguments{ -\item{P}{A convex polytope. It is an object from class (a) Hpolytope or (b) Vpolytope or (c) Zonotope.} - -\item{method}{Optional. The method to use for rounding, a) \code{'min_ellipsoid'} for the method based on mimimmum volume enclosing ellipsoid of a dataset, b) \code{'max_ellipsoid'} for the method based on maximum volume enclosed ellipsoid, (c) \code{'isotropy'} for the method based on svd decomposition. The default method is \code{'mee'} for all the representations.} - -\item{seed}{Optional. A fixed seed for the number generator.} -} -\value{ -A list with 4 elements: (a) a polytope of the same class as the input polytope class and (b) the element "T" which is the matrix of the inverse linear transformation that is applied on the input polytope, (c) the element "shift" which is the opposite vector of that which has shifted the input polytope, (d) the element "round_value" which is the determinant of the square matrix of the linear transformation that is applied on the input polytope. -} -\description{ -Given a convex H or V polytope or a zonotope as input this function brings the polytope in rounded position based on minimum volume enclosing ellipsoid of a pointset. -} -\examples{ -# round a 5d skinny cube -P = gen_skinny_cube(5) -listHpoly = round_polytope(P) - -# round a V-polytope (3d unit cube) -P = gen_cube(3, 'V') -ListVpoly = round_polytope(P) - -# round a 2-dimensional zonotope defined by 6 generators -Z = gen_rand_zonotope(2,6) -ListZono = round_polytope(Z) -} -\references{ -\cite{I.Z.Emiris and V. Fisikopoulos, -\dQuote{Practical polytope volume approximation,} \emph{ACM Trans. Math. Soft.,} 2018.}, - -\cite{Michael J. Todd and E. Alper Yildirim, -\dQuote{On Khachiyan’s Algorithm for the Computation of Minimum Volume Enclosing Ellipsoids,} \emph{Discrete Applied Mathematics,} 2007.} - -\cite{B. Cousins and S. Vempala, -\dQuote{A practical volume algorithm,} \emph{Math. Prog. Comp.,} 2016.}, - -\cite{Yin Zhang and Liyan Gao, -\dQuote{On Numerical Solution of the Maximum Volume Ellipsoid Problem,} \emph{SIAM Journal on Optimization,} 2003.}, -} diff --git a/R-proj/man/rounding.Rd b/R-proj/man/rounding.Rd deleted file mode 100644 index 75be33968..000000000 --- a/R-proj/man/rounding.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{rounding} -\alias{rounding} -\title{Internal rcpp function for the rounding of a convex polytope} -\usage{ -rounding(P, method = NULL, seed = NULL) -} -\arguments{ -\item{P}{A convex polytope (H- or V-representation or zonotope).} - -\item{method}{Optional. The method to use for rounding, a) \code{'min_ellipsoid'} for the method based on mimimmum volume enclosing ellipsoid of a uniform sample from P, b) \code{'max_ellipsoid'} for the method based on maximum volume enclosed ellipsoid in P, (c) \code{'isotropy'} for the method based on isotropy. The default method is \code{'min_ellipsoid'} for all the representations.} - -\item{seed}{Optional. A fixed seed for the number generator.} -} -\value{ -A numerical matrix that describes the rounded polytope, a numerical matrix of the inverse linear transofmation that is applied on the input polytope, the numerical vector the the input polytope is shifted and the determinant of the matrix of the linear transformation that is applied on the input polytope. -} -\description{ -Internal rcpp function for the rounding of a convex polytope -} -\keyword{internal} diff --git a/R-proj/man/sample_points.Rd b/R-proj/man/sample_points.Rd deleted file mode 100644 index e969e557d..000000000 --- a/R-proj/man/sample_points.Rd +++ /dev/null @@ -1,82 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{sample_points} -\alias{sample_points} -\title{Sample uniformly, normally distributed, or logconcave distributed points from a convex Polytope (H-polytope, V-polytope, zonotope or intersection of two V-polytopes).} -\usage{ -sample_points(P, n, random_walk = NULL, distribution = NULL, seed = NULL) -} -\arguments{ -\item{P}{A convex polytope. It is an object from class (a) Hpolytope or (b) Vpolytope or (c) Zonotope or (d) VpolytopeIntersection or (e) sparse_constraint_problem.} - -\item{n}{The number of points that the function is going to sample from the convex polytope.} - -\item{random_walk}{Optional. A list that declares the random walk and some related parameters as follows: -\itemize{ -\item{\code{walk} }{ A string to declare the random walk: i) \code{'CDHR'} for Coordinate Directions Hit-and-Run, ii) \code{'RDHR'} for Random Directions Hit-and-Run, iii) \code{'BaW'} for Ball Walk, iv) \code{'BiW'} for Billiard walk, v) \code{'dikin'} for dikin walk, vi) \code{'vaidya'} for vaidya walk, vii) \code{'john'} for john walk, viii) \code{'BCDHR'} boundary sampling by keeping the extreme points of CDHR or ix) \code{'BRDHR'} boundary sampling by keeping the extreme points of RDHR, x) \code{'NUTS'} for NUTS Hamiltonian Monte Carlo sampler (logconcave densities), xi) \code{'HMC'} for Hamiltonian Monte Carlo (logconcave densities), xii) CRHMC for Riemannian HMC with H-polytope constraints (uniform and general logconcave densities), xiii) \code{'ULD'} for Underdamped Langevin Dynamics using the Randomized Midpoint Method (logconcave densities), xiii) \code{'ExactHMC'} for exact Hamiltonian Monte Carlo with reflections (spherical Gaussian or exponential distribution). The default walk is \code{'aBiW'} for the uniform distribution, \code{'CDHR'} for the Gaussian distribution and H-polytopes and \code{'BiW'} or \code{'RDHR'} for the same distributions and V-polytopes and zonotopes. \code{'NUTS'} is the default sampler for logconcave densities and \code{'CRHMC'} for logconcave densities with H-polytope and sparse constrainted problems.} -\item{\code{walk_length} }{ The number of the steps per generated point for the random walk. The default value is \eqn{1}.} -\item{\code{nburns} }{ The number of points to burn before start sampling. The default value is \eqn{1}.} -\item{\code{starting_point} }{ A \eqn{d}-dimensional numerical vector that declares a starting point in the interior of the polytope for the random walk. The default choice is the center of the ball as that one computed by the function \code{inner_ball()}.} -\item{\code{BaW_rad} }{ The radius for the ball walk.} -\item{\code{L} }{ The maximum length of the billiard trajectory or the radius for the step of dikin, vaidya or john walk.} -\item{\code{solver}} {Specify ODE solver for logconcave sampling. Options are i) leapfrog, ii) euler iii) runge-kutta iv) richardson} -\item{\code{step_size} {Optionally chosen step size for logconcave sampling. Defaults to a theoretical value if not provided.}} -}} - -\item{distribution}{Optional. A list that declares the target density and some related parameters as follows: -\itemize{ -\item{\code{density} }{ A string: (a) \code{'uniform'} for the uniform distribution or b) \code{'gaussian'} for the multidimensional spherical distribution c) \code{logconcave} with form proportional to exp(-f(x)) where f(x) is L-smooth and m-strongly-convex d) \code{'exponential'} for the exponential distribution. The default target distribution is the uniform distribution.} -\item{\code{variance} }{ The variance of the multidimensional spherical gaussian or the exponential distribution. The default value is 1.} -\item{\code{mode} }{ A \eqn{d}-dimensional numerical vector that declares the mode of the Gaussian distribution. The default choice is the center of the as that one computed by the function \code{inner_ball()}.} -\item{\code{bias} }{ The bias vector for the exponential distribution. The default vector is \eqn{c_1 = 1} and \eqn{c_i = 0} for \eqn{i \neq 1}.} -\item{\code{L_} }{ Smoothness constant (for logconcave). } -\item{\code{m} }{ Strong-convexity constant (for logconcave). } -\item{\code{negative_logprob} }{ Negative log-probability (for logconcave). } -\item{\code{negative_logprob_gradient} }{ Negative log-probability gradient (for logconcave). } -}} - -\item{seed}{Optional. A fixed seed for the number generator.} -} -\value{ -A \eqn{d\times n} matrix that contains, column-wise, the sampled points from the convex polytope P. -} -\description{ -Sample uniformly, normally distributed, or logconcave distributed points from a convex Polytope (H-polytope, V-polytope, zonotope or intersection of two V-polytopes). -} -\examples{ -# uniform distribution from the 3d unit cube in H-representation using ball walk -P = gen_cube(3, 'H') -points = sample_points(P, n = 100, random_walk = list("walk" = "BaW", "walk_length" = 5)) - -# gaussian distribution from the 2d unit simplex in H-representation with variance = 2 -A = matrix(c(-1,0,0,-1,1,1), ncol=2, nrow=3, byrow=TRUE) -b = c(0,0,1) -P = Hpolytope$new(A,b) -points = sample_points(P, n = 100, distribution = list("density" = "gaussian", "variance" = 2)) - -# uniform points from the boundary of a 2-dimensional random H-polytope -P = gen_rand_hpoly(2,20) -points = sample_points(P, n = 100, random_walk = list("walk" = "BRDHR")) - -# For sampling from logconcave densities see the examples directory - -} -\references{ -\cite{Robert L. Smith, -\dQuote{Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed Over Bounded Regions,} \emph{Operations Research,} 1984.}, - -\cite{B.T. Polyak, E.N. Gryazina, -\dQuote{Billiard walk - a new sampling algorithm for control and optimization,} \emph{IFAC Proceedings Volumes,} 2014.}, - -\cite{Y. Chen, R. Dwivedi, M. J. Wainwright and B. Yu, -\dQuote{Fast MCMC Sampling Algorithms on Polytopes,} \emph{Journal of Machine Learning Research,} 2018.} - -\cite{Lee, Yin Tat, Ruoqi Shen, and Kevin Tian, -\dQuote{"Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo,"} \emph{arXiv preprint arXiv:2002.04121}, 2020.} - -\cite{Shen, Ruoqi, and Yin Tat Lee, -\dQuote{"The randomized midpoint method for log-concave sampling.",} \emph{Advances in Neural Information Processing Systems}, 2019.} - -\cite{Augustin Chevallier, Sylvain Pion, Frederic Cazals, -\dQuote{"Hamiltonian Monte Carlo with boundary reflections, and application to polytope volume calculations,"} \emph{Research Report preprint hal-01919855}, 2018.} -} diff --git a/R-proj/man/sparse_constraint_problem.Rd b/R-proj/man/sparse_constraint_problem.Rd deleted file mode 100644 index 535dc8426..000000000 --- a/R-proj/man/sparse_constraint_problem.Rd +++ /dev/null @@ -1,26 +0,0 @@ -\name{sparse_constraint_problem} -\alias{sparse_constraint_problem} -\title{An \code{R} class to represent sparse constraint problems.} - -\description{ -A constraint problem is defined by a set of linear inequalities and equalities or equivalently a \eqn{d}-dimensional constraint problem is defined by a \eqn{mineq\times d} matrix Aineq and a \eqn{mineq}-dimensional vector bineq, s.t.: \eqn{Aineqx\leq bineq}, a \eqn{meq\times d} matrix Aeq and a \eqn{meq}-dimensional vector beq, s.t.: \eqn{Aeqx\eq beq} and two \eqn{d} vectors lb, ub such that \eqn{lb\leq x \leq ub}. -} -\section{Fields}{ -\itemize{ -\item{\code{Aineq} }{\eqn{mineq\times d} sparse matrix Aineq} - -\item{\code{bineq} }{\eqn{mineq}-dimensional vector bineq} - -\item{\code{Aeq} }{\eqn{meq\times d} sparse matrix Aeq} - -\item{\code{beq} }{\eqn{meq}-dimensional vector beq} - -\item{\code{lb} }{\eqn{d}-dimensional vector bineq} - -\item{\code{ub} }{\eqn{d}-dimensional vector bineq} - -\item{\code{type} }{An integer that declares the representation of the polytope. For sparse_constraint_problem the default value is 5.} - -\item{\code{dimension} }{The dimension of the polytope.} - -}} diff --git a/R-proj/man/volume.Rd b/R-proj/man/volume.Rd deleted file mode 100644 index cd325c87f..000000000 --- a/R-proj/man/volume.Rd +++ /dev/null @@ -1,55 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{volume} -\alias{volume} -\title{The main function for volume approximation of a convex Polytope (H-polytope, V-polytope, zonotope or intersection of two V-polytopes). It returns a list with two elements: (a) the logarithm of the estimated volume and (b) the estimated volume} -\usage{ -volume(P, settings = NULL, rounding = NULL, seed = NULL) -} -\arguments{ -\item{P}{A convex polytope. It is an object from class a) Hpolytope or b) Vpolytope or c) Zonotope or d) VpolytopeIntersection.} - -\item{settings}{Optional. A list that declares which algorithm, random walk and values of parameters to use, as follows: -\itemize{ -\item{\code{algorithm} }{ A string to set the algorithm to use: a) \code{'CB'} for CB algorithm, b) \code{'SoB'} for SOB algorithm or b) \code{'CG'} for CG algorithm. The defalut algorithm is \code{'CB'}.} -\item{\code{error} }{ A numeric value to set the upper bound for the approximation error. The default value is \eqn{1} for SOB algorithm and \eqn{0.1} otherwise.} -\item{\code{random_walk} }{ A string that declares the random walk method: a) \code{'CDHR'} for Coordinate Directions Hit-and-Run, b) \code{'RDHR'} for Random Directions Hit-and-Run, c) \code{'BaW'} for Ball Walk, or \code{'BiW'} for Billiard walk. For CB algorithm the default walk is \code{'BiW'}. For CG and SOB algorithms the default walk is \code{'CDHR'} for H-polytopes and \code{'RDHR'} for the other representations.} -\item{\code{walk_length} }{ An integer to set the number of the steps for the random walk. The default value is \eqn{\lfloor 10 + d/10\rfloor} for \code{'SOB'} and \eqn{1} otherwise.} -\item{\code{win_len} }{ The length of the sliding window for CB or CG algorithm. The default value is \eqn{250} for CB with BiW and \eqn{400+3d^2} for CB and any other random walk and \eqn{500+4d^2} for CG.} -\item{\code{hpoly} }{ A boolean parameter to use H-polytopes in MMC of CB algorithm when the input polytope is a zonotope. The default value is \code{TRUE} when the order of the zonotope is \eqn{<5}, otherwise it is \code{FALSE}.} -}} - -\item{rounding}{Optional. A string parameter to request a rounding method to be applied in the input polytope before volume computation: a) \code{'min_ellipsoid'}, b) \code{'svd'}, c) \code{'max_ellipsoid'} and d) \code{'none'} for no rounding.} - -\item{seed}{Optional. A fixed seed for the number generator.} -} -\value{ -The approximation of the volume of a convex polytope. -} -\description{ -For the volume approximation can be used three algorithms. Either CoolingBodies (CB) or SequenceOfBalls (SOB) or CoolingGaussian (CG). An H-polytope with \eqn{m} facets is described by a \eqn{m\times d} matrix \eqn{A} and a \eqn{m}-dimensional vector \eqn{b}, s.t.: \eqn{P=\{x\ |\ Ax\leq b\} }. A V-polytope is defined as the convex hull of \eqn{m} \eqn{d}-dimensional points which correspond to the vertices of P. A zonotope is desrcibed by the Minkowski sum of \eqn{m} \eqn{d}-dimensional segments. -} -\examples{ - -# calling SOB algorithm for a H-polytope (5d unit simplex) -HP = gen_cube(5,'H') -pair_vol = volume(HP) - -# calling CG algorithm for a V-polytope (3d simplex) -VP = gen_simplex(3,'V') -pair_vol = volume(VP, settings = list("algorithm" = "CG")) - -# calling CG algorithm for a 2-dimensional zonotope defined as the Minkowski sum of 4 segments -Z = gen_rand_zonotope(2, 4) -pair_vol = volume(Z, settings = list("random_walk" = "RDHR", "walk_length" = 2)) - -} -\references{ -\cite{I.Z.Emiris and V. Fisikopoulos, -\dQuote{Practical polytope volume approximation,} \emph{ACM Trans. Math. Soft.,} 2018.}, - -\cite{A. Chalkis and I.Z.Emiris and V. Fisikopoulos, -\dQuote{Practical Volume Estimation by a New Annealing Schedule for Cooling Convex Bodies,} \emph{CoRR, abs/1905.05494,} 2019.}, - -\cite{B. Cousins and S. Vempala, \dQuote{A practical volume algorithm,} \emph{Springer-Verlag Berlin Heidelberg and The Mathematical Programming Society,} 2015.} -} diff --git a/R-proj/man/writeSdpaFormatFile.Rd b/R-proj/man/writeSdpaFormatFile.Rd deleted file mode 100644 index 7c791dce0..000000000 --- a/R-proj/man/writeSdpaFormatFile.Rd +++ /dev/null @@ -1,34 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{writeSdpaFormatFile} -\alias{writeSdpaFormatFile} -\title{Write a SDPA format file} -\usage{ -writeSdpaFormatFile( - spectrahedron = NULL, - objectiveFunction = NULL, - outputFile = NULL -) -} -\arguments{ -\item{spectrahedron}{A spectrahedron in n dimensions; must be an object of class Spectrahedron} - -\item{objectiveFunction}{A numerical vector of length n} - -\item{outputFile}{Name of the output file} -} -\description{ -Outputs a spectrahedron (the matrices defining a linear matrix inequality) and a vector (the objective function) -to a SDPA format file. -} -\examples{ -\dontrun{ -A0 = matrix(c(-1,0,0,0,-2,1,0,1,-2), nrow=3, ncol=3, byrow = TRUE) -A1 = matrix(c(-1,0,0,0,0,1,0,1,0), nrow=3, ncol=3, byrow = TRUE) -A2 = matrix(c(0,0,-1,0,0,0,-1,0,0), nrow=3, ncol=3, byrow = TRUE) -lmi = list(A0, A1, A2) -S = Spectrahedron$new(lmi); -objFunction = c(1,1) -writeSdpaFormatFile(S, objFunction, "output.txt") -} -} diff --git a/R-proj/man/zono_approx.Rd b/R-proj/man/zono_approx.Rd deleted file mode 100644 index 9c27c369c..000000000 --- a/R-proj/man/zono_approx.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{zono_approx} -\alias{zono_approx} -\title{An internal Rccp function for the over-approximation of a zonotope} -\usage{ -zono_approx(Z, fit_ratio = NULL, settings = NULL, seed = NULL) -} -\arguments{ -\item{Z}{A zonotope.} - -\item{fit_ratio}{Optional. A boolean parameter to request the computation of the ratio of fitness.} - -\item{settings}{Optional. A list that declares the values of the parameters of CB algorithm.} - -\item{seed}{Optional. A fixed seed for the number generator.} -} -\value{ -A List that contains a numerical matrix that describes the PCA approximation as a H-polytope and the ratio of fitness. -} -\description{ -An internal Rccp function for the over-approximation of a zonotope -} -\keyword{internal} diff --git a/R-proj/man/zonotope_approximation.Rd b/R-proj/man/zonotope_approximation.Rd deleted file mode 100644 index 152a0b0a7..000000000 --- a/R-proj/man/zonotope_approximation.Rd +++ /dev/null @@ -1,39 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/zonotope_approximation.R -\name{zonotope_approximation} -\alias{zonotope_approximation} -\title{A function to over-approximate a zonotope with PCA method and to evaluate the approximation by computing a ratio of fitness.} -\usage{ -zonotope_approximation(Z, fit_ratio = NULL, settings = NULL, seed = NULL) -} -\arguments{ -\item{Z}{A zonotope.} - -\item{fit_ratio}{Optional. A boolean parameter to request the computation of the ratio of fitness.} - -\item{settings}{Optional. A list that declares the values of the parameters of CB algorithm as follows: -\itemize{ -\item{\code{error} }{ A numeric value to set the upper bound for the approximation error. The default value is \eqn{0.1}.} -\item{\code{walk_length} }{ An integer to set the number of the steps for the random walk. The default value is \eqn{1}.} -\item{\code{win_len} }{ The length of the sliding window for CB algorithm. The default value is \eqn{200}.} -\item{\code{hpoly} }{ A boolean parameter to use H-polytopes in MMC of CB algorithm. The default value is \code{TRUE} when the order of the zonotope is \eqn{<5}, otherwise it is \code{FALSE}.} -}} - -\item{seed}{Optional. A fixed seed for the number generator.} -} -\value{ -A list that contains the approximation body in H-representation and the ratio of fitness -} -\description{ -For the evaluation of the PCA method the exact volume of the approximation body is computed and the volume of the input zonotope is computed by CoolingBodies algorithm. The ratio of fitness is \eqn{R=vol(P) / vol(P_{red})}, where \eqn{P_{red}} is the approximate polytope. -} -\examples{ -# over-approximate a 2-dimensional zonotope with 10 generators and compute the ratio of fitness -Z = gen_rand_zonotope(2,12) -retList = zonotope_approximation(Z = Z) - -} -\references{ -\cite{A.K. Kopetzki and B. Schurmann and M. Althoff, -\dQuote{Methods for Order Reduction of Zonotopes,} \emph{IEEE Conference on Decision and Control,} 2017.} -} diff --git a/R-proj/src/Makevars b/R-proj/src/Makevars deleted file mode 100644 index 12623b8b9..000000000 --- a/R-proj/src/Makevars +++ /dev/null @@ -1,17 +0,0 @@ -PKG_CPPFLAGS= -I../../external/boost -I../../external/autodiff -I../../external/LPsolve_src/run_headers -I../../external/minimum_ellipsoid -I../../include - -PKG_CXXFLAGS= -Wno-deprecated-declarations -lm -ldl -Wno-ignored-attributes -DBOOST_NO_AUTO_PTR -DDISABLE_NLP_ORACLES - -PKG_LIBS=-LRproj_externals/lp_solve -llp_solve -L../../external/PackedCSparse/qd -lqd $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) - -$(SHLIB): Rproj_externals/lp_solve/liblp_solve.a ../../external/PackedCSparse/qd/libqd.a - -Rproj_externals/lp_solve/liblp_solve.a: - @(cd Rproj_externals/lp_solve && $(MAKE) liblp_solve.a \ - CC="$(CC)" CPPFLAGS="$(CPPFLAGS)" CFLAGS="$(CFLAGS)" \ - CPICFLAGS="$(CPICFLAGS)" AR="$(AR)" RANLIB="$(RANLIB)") - -../../external/PackedCSparse/qd/libqd.a: - @(cd ../../PackedCSparse/qd && $(MAKE) libqd.a \ - CC="$(CC)" CPPFLAGS="$(CPPFLAGS)" CFLAGS="$(CFLAGS)" \ - CPICFLAGS="$(CPICFLAGS)" AR="$(AR)") diff --git a/R-proj/src/Makevars.win b/R-proj/src/Makevars.win deleted file mode 100644 index 83ed4e0c6..000000000 --- a/R-proj/src/Makevars.win +++ /dev/null @@ -1,17 +0,0 @@ -PKG_CPPFLAGS=-I../../external/boost -I../../external/LPsolve_src/run_headers -I../../external/minimum_ellipsoid -I../../include -I../../include/convex_bodies/spectrahedra -PKG_CXXFLAGS= -Wno-deprecated-declarations -lm -ldl -Wno-ignored-attributes -DBOOST_NO_AUTO_PTR -DDISABLE_NLP_ORACLES - -PKG_LIBS=-LRproj_externals/lp_solve -llp_solve -L../../external/PackedCSparse/qd -lqd $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) - -$(SHLIB): Rproj_externals/lp_solve/liblp_solve.a ../../external/PackedCSparse/qd/libqd.a - -Rproj_externals/lp_solve/liblp_solve.a: - @(cd Rproj_externals/lp_solve && $(MAKE) liblp_solve.a \ - CC="$(CC)" CPPFLAGS="$(CPPFLAGS) -DUSRDLL -DINLINE=static" \ - CFLAGS="$(CFLAGS)" CPICFLAGS="$(CPICFLAGS)" AR="$(AR)" \ - RANLIB="$(RANLIB)") - -../../external/PackedCSparse/qd/libqd.a: - @(cd ../../PackedCSparse/qd && $(MAKE) libqd.a \ - CC="$(CC)" CPPFLAGS="$(CPPFLAGS)" CFLAGS="$(CFLAGS)" \ - CPICFLAGS="$(CPICFLAGS)" AR="$(AR)") diff --git a/R-proj/src/copula.cpp b/R-proj/src/copula.cpp deleted file mode 100644 index 0e8069ea2..000000000 --- a/R-proj/src/copula.cpp +++ /dev/null @@ -1,114 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2018 Vissarion Fisikopoulos -// Copyright (c) 2018 Apostolos Chalkis - -//Contributed and/or modified by Apostolos Chalkis, as part of Google Summer of Code 2018 program. - - -#include -#include -#include -#include "convex_bodies/ellipsoid.h" -#include "cartesian_geom/cartesian_kernel.h" -#include -#include -#include "sampling/simplex.hpp" -#include "volume/copulas.h" - -//' Construct a copula using uniform sampling from the unit simplex -//' -//' Given two families of parallel hyperplanes or a family of parallel hyperplanes and a family of concentric ellispoids centered at the origin intersecting the canonical simplex, this function uniformly samples from the canonical simplex and construct an approximation of the bivariate probability distribution, called copula (see \url{https://en.wikipedia.org/wiki/Copula_(probability_theory)}). -//' At least two families of hyperplanes or one family of hyperplanes and one family of ellipsoids have to be given as input. -//' -//' @param r1 The \eqn{d}-dimensional normal vector of the first family of parallel hyperplanes. -//' @param r2 Optional. The \eqn{d}-dimensional normal vector of the second family of parallel hyperplanes. -//' @param sigma Optional. The \eqn{d\times d} symmetric positive semidefine matrix that describes the family of concentric ellipsoids centered at the origin. -//' @param m The number of the slices for the copula. The default value is 100. -//' @param n The number of points to sample. The default value is \eqn{5\cdot 10^5}. -//' @param seed Optional. A fixed seed for the number generator. -//' -//' @references \cite{L. Cales, A. Chalkis, I.Z. Emiris, V. Fisikopoulos, -//' \dQuote{Practical volume computation of structured convex bodies, and an application to modeling portfolio dependencies and financial crises,} \emph{Proc. of Symposium on Computational Geometry, Budapest, Hungary,} 2018.} -//' -//' @return A \eqn{m\times m} numerical matrix that corresponds to a copula. -//' @examples -//' # compute a copula for two random families of parallel hyperplanes -//' h1 = runif(n = 10, min = 1, max = 1000) -//' h1 = h1 / 1000 -//' h2=runif(n = 10, min = 1, max = 1000) -//' h2 = h2 / 1000 -//' cop = copula(r1 = h1, r2 = h2, m = 10, n = 100000) -//' -//' # compute a copula for a family of parallel hyperplanes and a family of conentric ellipsoids -//' h = runif(n = 10, min = 1, max = 1000) -//' h = h / 1000 -//' E = replicate(10, rnorm(20)) -//' E = cov(E) -//' cop = copula(r1 = h, sigma = E, m = 10, n = 100000) -//' -//' @export -// [[Rcpp::export]] -Rcpp::NumericMatrix copula (Rcpp::Nullable r1, - Rcpp::Nullable r2 = R_NilValue, - Rcpp::Nullable sigma = R_NilValue, - Rcpp::Nullable m = R_NilValue, - Rcpp::Nullable n = R_NilValue, - Rcpp::Nullable seed = R_NilValue){ - - typedef double NT; - typedef Cartesian Kernel; - typedef typename Kernel::Point Point; - typedef boost::mt19937 RNGType; - typedef Eigen::Matrix MT; - typedef Eigen::Matrix VT; - typedef Ellipsoid CopEll; - unsigned int num_slices = 100, numpoints = 500000; - - if (m.isNotNull()) { - num_slices = Rcpp::as(m); - } - - if (n.isNotNull()) { - numpoints = Rcpp::as(n); - } - - double seed3 = (!seed.isNotNull()) ? std::numeric_limits::signaling_NaN() : Rcpp::as(seed); - - Rcpp::NumericMatrix copula(num_slices, num_slices); - std::vector > StdCopula; - unsigned int dim = Rcpp::as >(r1).size(), i, j; - - std::vector hyp1 = Rcpp::as >(r1); - if (r2.isNotNull()) { - - std::vector hyp2 = Rcpp::as < std::vector < NT > > (r2); - StdCopula = twoParHypFam(dim, numpoints, num_slices, hyp1, hyp2, seed3); - - } else if (sigma.isNotNull()) { - - std::vector > Gin(dim, std::vector(dim)); - MT EE = Rcpp::as(sigma); - for (int i=0; i(dim, numpoints, num_slices, hyp1, Ell, seed3); - } else { - - throw Rcpp::exception("You have to give as input either two families of hyperplanes or one family of hyperplanes and one family of ellipsoids!"); - - } - - for(int i=0; i -#include -#include -#include "cartesian_geom/cartesian_kernel.h" -#include -#include -#include -#include -#include "volume/volume_sequence_of_balls.hpp" -#include "sampling/simplex.hpp" - -//' Sample perfect uniformly distributed points from well known convex bodies: (a) the unit simplex, (b) the canonical simplex, (c) the boundary of a hypersphere or (d) the interior of a hypersphere. -//' -//' The \eqn{d}-dimensional unit simplex is the set of points \eqn{\vec{x}\in \R^d}, s.t.: \eqn{\sum_i x_i\leq 1}, \eqn{x_i\geq 0}. The \eqn{d}-dimensional canonical simplex is the set of points \eqn{\vec{x}\in \R^d}, s.t.: \eqn{\sum_i x_i = 1}, \eqn{x_i\geq 0}. -//' -//' @param body A list to request exact uniform sampling from special well known convex bodies through the following input parameters: -//' \itemize{ -//' \item{\code{type} }{ A string that declares the type of the body for the exact sampling: a) \code{'unit_simplex'} for the unit simplex, b) \code{'canonical_simplex'} for the canonical simplex, c) \code{'hypersphere'} for the boundary of a hypersphere centered at the origin, d) \code{'ball'} for the interior of a hypersphere centered at the origin.} -//' \item{\code{dimension} }{ An integer that declares the dimension when exact sampling is enabled for a simplex or a hypersphere.} -//' \item{\code{radius} }{ The radius of the \eqn{d}-dimensional hypersphere. The default value is \eqn{1}.} -//' } -//' @param n The number of points that the function is going to sample. -//' @param seed Optional. A fixed seed for the number generator. -//' -//' @references \cite{R.Y. Rubinstein and B. Melamed, -//' \dQuote{Modern simulation and modeling} \emph{ Wiley Series in Probability and Statistics,} 1998.} -//' @references \cite{A Smith, Noah and W Tromble, Roy, -//' \dQuote{Sampling Uniformly from the Unit Simplex,} \emph{ Center for Language and Speech Processing Johns Hopkins University,} 2004.} -//' -//' @return A \eqn{d\times n} matrix that contains, column-wise, the sampled points from the convex polytope P. -//' @examples -//' # 100 uniform points from the 2-d unit ball -//' points = direct_sampling(n = 100, body = list("type" = "ball", "dimension" = 2)) -//' @export -// [[Rcpp::export]] -Rcpp::NumericMatrix direct_sampling(Rcpp::Nullable body, - Rcpp::Nullable n, - Rcpp::Nullable seed = R_NilValue) { - - typedef double NT; - typedef Cartesian Kernel; - typedef boost::mt19937 RNGType2; - typedef typename Kernel::Point Point; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - typedef BoostRandomNumberGenerator RNGType; - - int dim, numpoints; - NT radius = 1.0; - std::list randPoints; - - if (!Rcpp::as(body).containsElementNamed("dimension")) { - throw Rcpp::exception("Dimension has to be given as input!"); - } - dim = Rcpp::as(Rcpp::as(body)["dimension"]); - if (dim <=1) throw Rcpp::exception("Dimension has to be larger than 1!"); - RNGType rng(dim); - - if (seed.isNotNull()) { - unsigned seed2 = Rcpp::as(seed); - rng.set_seed(seed2); - } - double seed3 = (!seed.isNotNull()) ? std::numeric_limits::signaling_NaN() : Rcpp::as(seed); - //RNGType rng2(5); - - numpoints = (!n.isNotNull()) ? 100 : Rcpp::as(n); - - numpoints = Rcpp::as(n); - if (numpoints <= 0) throw Rcpp::exception("The number of samples has to be a positice integer!"); - - - if (Rcpp::as(body).containsElementNamed("radius")) { - - radius = Rcpp::as(Rcpp::as(body)["radius"]); - if (radius <= NT(0)) throw Rcpp::exception("Radius has to be a positive number!"); - - } - if (!Rcpp::as(body).containsElementNamed("type")) { - - throw Rcpp::exception("The kind of body has to be given as input!"); - - } - if (Rcpp::as(Rcpp::as(body)["type"]).compare(std::string("hypersphere"))==0) { - - for (unsigned int k = 0; k < numpoints; ++k) { - randPoints.push_back(GetPointOnDsphere::apply(dim, radius, rng)); - } - - } else if (Rcpp::as(Rcpp::as(body)["type"]).compare(std::string("ball"))==0) { - - for (unsigned int k = 0; k < numpoints; ++k) { - randPoints.push_back(GetPointInDsphere::apply(dim, radius, rng)); - } - - } else if (Rcpp::as(Rcpp::as(body)["type"]).compare(std::string("unit_simplex"))==0) { - - Sam_Unit(dim, numpoints, randPoints, seed3); - - } else if (Rcpp::as(Rcpp::as(body)["type"]).compare(std::string("canonical_simplex"))==0) { - - Sam_Canon_Unit(dim, numpoints, randPoints, seed3); - - } else { - - throw Rcpp::exception("Wrong input!"); - - } - - MT RetMat(dim, numpoints); - unsigned int jj = 0; - - for (typename std::list::iterator rpit = randPoints.begin(); rpit!=randPoints.end(); rpit++, jj++) { - RetMat.col(jj) = rpit->getCoefficients(); - } - return Rcpp::wrap(RetMat); -} diff --git a/R-proj/src/ess.cpp b/R-proj/src/ess.cpp deleted file mode 100644 index 15730557c..000000000 --- a/R-proj/src/ess.cpp +++ /dev/null @@ -1,44 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 20014-2020 Vissarion Fisikopoulos -// Copyright (c) 2018-2020 Apostolos Chalkis - -//Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. - -#include -#include -#include -#include -#include -#include -#include -#include "diagnostics/effective_sample_size.hpp" - -//' Gelman-Rubin and Brooks-Gelman Potential Scale Reduction Factor (PSRF) for each marginal -//' -//' @param samples A matrix that contans column-wise the sampled points from a geometric random walk. -//' -//' @references \cite{Gelman, A. and Rubin, D. B., -//' \dQuote{Inference from iterative simulation using multiple sequences,} \emph{Statistical Science,} 1992.} -//' -//' @references \cite{Brooks, S. and Gelman, A., -//' \dQuote{General Methods for Monitoring Convergence of Iterative Simulations,} \emph{Journal of Computational and Graphical Statistics,} 1998.} -//' -//' @return A vector that contains the values of PSRF for each coordinate -//' -//' @export -// [[Rcpp::export]] -Rcpp::NumericVector ess(Rcpp::NumericMatrix samples) -{ - typedef double NT; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - - MT samples_ = Rcpp::as(samples); - unsigned int min_ess = 0; - VT Neff = effective_sample_size(samples_, min_ess); - - return Rcpp::wrap(Neff); -} diff --git a/R-proj/src/exact_vol.cpp b/R-proj/src/exact_vol.cpp deleted file mode 100644 index d60d8fa4b..000000000 --- a/R-proj/src/exact_vol.cpp +++ /dev/null @@ -1,104 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2019 Vissarion Fisikopoulos -// Copyright (c) 2018-2019 Apostolos Chalkis - - -#include -#include -#include -#include -#include -#include -#include -#include "cartesian_geom/cartesian_kernel.h" -#include "convex_bodies/hpolytope.h" -#include "convex_bodies/vpolytope.h" -#include "convex_bodies/zpolytope.h" -#include "volume/exact_vols.h" - -template -FT factorial(FT n) -{ - return (n == 1 || n == 0) ? 1 : factorial(n - 1) * n; -} - -//' Compute the exact volume of (a) a zonotope (b) an arbitrary simplex in V-representation or (c) if the volume is known and declared by the input object. -//' -//' Given a zonotope (as an object of class Zonotope), this function computes the sum of the absolute values of the determinants of all the \eqn{d \times d} submatrices of the \eqn{m\times d} matrix \eqn{G} that contains row-wise the \eqn{m} \eqn{d}-dimensional segments that define the zonotope. -//' For an arbitrary simplex that is given in V-representation this function computes the absolute value of the determinant formed by the simplex's points assuming it is shifted to the origin. -//' -//' @param P A polytope -//' -//' @references \cite{E. Gover and N. Krikorian, -//' \dQuote{Determinants and the Volumes of Parallelotopes and Zonotopes,} \emph{Linear Algebra and its Applications, 433(1), 28 - 40,} 2010.} -//' -//' @return The exact volume of the input polytope, for zonotopes, simplices in V-representation and polytopes with known exact volume -//' @examples -//' -//' # compute the exact volume of a 5-dimensional zonotope defined by the Minkowski sum of 10 segments -//' Z = gen_rand_zonotope(2, 5) -//' vol = exact_vol(Z) -//' -//' \donttest{# compute the exact volume of a 2-d arbitrary simplex -//' V = matrix(c(2,3,-1,7,0,0),ncol = 2, nrow = 3, byrow = TRUE) -//' P = Vpolytope$new(V) -//' vol = exact_vol(P) -//' } -//' -//' # compute the exact volume the 10-dimensional cross polytope -//' P = gen_cross(10,'V') -//' vol = exact_vol(P) -//' @export -// [[Rcpp::export]] -double exact_vol(Rcpp::Nullable P) { - - typedef double NT; - typedef Cartesian Kernel; - typedef typename Kernel::Point Point; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - - if (NT(Rcpp::as(P).field("volume")) > 0.0) { - return NT(Rcpp::as(P).field("volume")); - } - - int type = Rcpp::as(P).field("type"), dim; - NT vol; - - if (type == 2) { - - dim = Rcpp::as(P).field("dimension"); - - if (Rcpp::as(Rcpp::as(P).field("V")).rows() == - Rcpp::as(Rcpp::as(P).field("V")).cols() + 1) { - - MT V = Rcpp::as(Rcpp::as(P).field("V")).transpose(), V2(dim,dim); - VT v0 = V.col(dim); - - for (int i = 0; i < dim; ++i) { - V2.col(i) = V.col(i) - v0; - } - vol = std::abs(V2.determinant()) / factorial(NT(dim)); - - } else { - throw Rcpp::exception("Volume unknown!"); - } - - } else if (type == 3) { - - typedef Zonotope zonotope; - dim = Rcpp::as(P).field("dimension"); - - zonotope ZP(dim, Rcpp::as(Rcpp::as(P).field("G")), - VT::Ones(Rcpp::as(Rcpp::as(P).field("G")).rows())); - vol = exact_zonotope_vol(ZP); - - } else { - throw Rcpp::exception("Volume unknown!"); - } - - return vol; -} diff --git a/R-proj/src/extractMatPoly.h b/R-proj/src/extractMatPoly.h deleted file mode 100644 index 5adb9d56b..000000000 --- a/R-proj/src/extractMatPoly.h +++ /dev/null @@ -1,37 +0,0 @@ -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2018 Vissarion Fisikopoulos, Apostolos Chalkis - -//Contributed and/or modified by Apostolos Chalkis, as part of Google Summer of Code 2018 program. - -// VolEsti is free software: you can redistribute it and/or modify it -// under the terms of the GNU Lesser General Public License as published by -// the Free Software Foundation, either version 3 of the License, or (at -// your option) any later version. -// -// VolEsti is distributed in the hope that it will be useful, but WITHOUT -// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or -// FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License -// for more details. -// -// See the file COPYING.LESSER for the text of the GNU Lesser General -// Public License. If you did not receive this file along with HeaDDaCHe, -// see . - - -#ifndef EXTRACTMATPOLY_H -#define EXTRACTMATPOLY_H - -// Take a H or a V-polytope and return a numerical matrix in ine or ext format respectively -template -Rcpp::NumericMatrix extractMatPoly(Polytope P) { - - typedef typename Polytope::MT MT; - - MT Mat(P.get_mat().rows(), P.dimension()+1); - Mat << P.get_vec(), P.get_mat(); - - return Rcpp::wrap(Mat); -} - -#endif diff --git a/R-proj/src/frustum_of_simplex.cpp b/R-proj/src/frustum_of_simplex.cpp deleted file mode 100644 index 6b191079e..000000000 --- a/R-proj/src/frustum_of_simplex.cpp +++ /dev/null @@ -1,41 +0,0 @@ -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2018 Vissarion Fisikopoulos -// Copyright (c) 2018 Apostolos Chalkis - -#include -#include -#include "volume/exact_vols.h" - -//' Compute the percentage of the volume of the simplex that is contained in the intersection of a half-space and the simplex. -//' -//' A half-space \eqn{H} is given as a pair of a vector \eqn{a\in R^d} and a scalar \eqn{z0\in R} s.t.: \eqn{a^Tx\leq z0}. This function calls the Ali's version of the Varsi formula to compute a frustum of the simplex. -//' -//' @param a A \eqn{d}-dimensional vector that defines the direction of the hyperplane. -//' @param z0 The scalar that defines the half-space. -//' -//' @references \cite{Varsi, Giulio, -//' \dQuote{The multidimensional content of the frustum of the simplex,} \emph{Pacific J. Math. 46, no. 1, 303--314,} 1973.} -//' -//' @references \cite{Ali, Mir M., -//' \dQuote{Content of the frustum of a simplex,} \emph{ Pacific J. Math. 48, no. 2, 313--322,} 1973.} -//' -//' @return The percentage of the volume of the simplex that is contained in the intersection of a given half-space and the simplex. -//' -//' @examples -//' # compute the frustum of H: -x1+x2<=0 -//' a=c(-1,1) -//' z0=0 -//' frustum = frustum_of_simplex(a, z0) -//' @export -// [[Rcpp::export]] -double frustum_of_simplex(Rcpp::NumericVector a, double z0){ - - unsigned int dim = a.size(); - if (dim < 2) { - throw Rcpp::exception("Dimension has to be greater than 2"); - } - std::vector hyp = Rcpp::as >(a); - - return vol_Ali(hyp, -z0, dim); -} diff --git a/R-proj/src/geweke.cpp b/R-proj/src/geweke.cpp deleted file mode 100644 index 4c41b227e..000000000 --- a/R-proj/src/geweke.cpp +++ /dev/null @@ -1,47 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 20014-2020 Vissarion Fisikopoulos -// Copyright (c) 2020 Apostolos Chalkis - -//Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. - - -#include -#include -#include -#include -#include -#include -#include -#include "diagnostics/geweke.hpp" - -//' Geweke's MCMC diagnostic -//' -//' @param samples A matrix that contans column-wise the sampled points from a geometric random walk. -//' @param frac_first Optional. The portion of the first in order points in matrix samples. -//' @param frac_last Optional. The portion of the last in order points in matrix samples. -//' -//' @references \cite{Geweke, J., -//' \dQuote{Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments,} \emph{ In Bayesian Statistics 4. Proceedings of the Fourth Valencia International Meeting,} 1992.} -//' -//' @return A boolean to denote if the result of Geweke diagnostic: (i) false if the null hypothesis is rejected, (ii) true if the null hypothesis is not rejected. -//' -//' @export -// [[Rcpp::export]] -bool geweke(Rcpp::NumericMatrix samples, - Rcpp::Nullable frac_first = R_NilValue, - Rcpp::Nullable frac_last = R_NilValue) -{ - typedef double NT; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - - NT frac_1, frac_2; - - frac_1 = (!frac_first.isNotNull()) ? NT(0.1) : Rcpp::as(frac_first); - frac_2 = (!frac_last.isNotNull()) ? NT(0.5) : Rcpp::as(frac_last); - - return perform_geweke(Rcpp::as(samples), frac_1, frac_2); -} diff --git a/R-proj/src/inner_ball.cpp b/R-proj/src/inner_ball.cpp deleted file mode 100644 index 8472ad3b8..000000000 --- a/R-proj/src/inner_ball.cpp +++ /dev/null @@ -1,98 +0,0 @@ -// Copyright (c) 2012-2018 Vissarion Fisikopoulos -// Copyright (c) 2018 Apostolos Chalkis - -#include -#include -#include -#include "cartesian_geom/cartesian_kernel.h" -#include -#include -#include -#include -#include "volume/volume_sequence_of_balls.hpp" -#include "preprocess/max_inscribed_ball.hpp" - -//' Compute an inscribed ball of a convex polytope -//' -//' For a H-polytope described by a \eqn{m\times d} matrix \eqn{A} and a \eqn{m}-dimensional vector \eqn{b}, s.t.: \eqn{P=\{x\ |\ Ax\leq b\} }, this function computes the largest inscribed ball (Chebychev ball) by solving the corresponding linear program. -//' For both zonotopes and V-polytopes the function computes the minimum \eqn{r} s.t.: \eqn{ r e_i \in P} for all \eqn{i=1, \dots ,d}. Then the ball centered at the origin with radius \eqn{r/ \sqrt{d}} is an inscribed ball. -//' -//' @param P A convex polytope. It is an object from class (a) Hpolytope or (b) Vpolytope or (c) Zonotope or (d) VpolytopeIntersection. -//' @param lpsolve Optional. A boolean variable to compute the Chebychev ball of an H-polytope using the lpsolve library. -//' -//' @return A \eqn{(d+1)}-dimensional vector that describes the inscribed ball. The first \eqn{d} coordinates corresponds to the center of the ball and the last one to the radius. -//' -//' @examples -//' # compute the Chebychev ball of the 2d unit simplex -//' P = gen_cube(10,'H') -//' ball_vec = inner_ball(P) -//' -//' # compute an inscribed ball of the 3-dimensional unit cube in V-representation -//' P = gen_cube(3, 'V') -//' ball_vec = inner_ball(P, lpsolve = TRUE) -//' @export -// [[Rcpp::export]] -Rcpp::NumericVector inner_ball(Rcpp::Reference P, - Rcpp::Nullable lpsolve = R_NilValue) { - - typedef double NT; - typedef Cartesian Kernel; - typedef typename Kernel::Point Point; - typedef BoostRandomNumberGenerator RNGType; - typedef HPolytope Hpolytope; - typedef VPolytope Vpolytope; - typedef Zonotope zonotope; - typedef IntersectionOfVpoly InterVP; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - unsigned int n = P.field("dimension"), type = P.field("type"); - - std::pair InnerBall; - bool lp_solve = (lpsolve.isNotNull()) ? Rcpp::as(lpsolve) : false; - - switch (type) { - case 1: { - // Hpolytope - Hpolytope HP(n, Rcpp::as(P.field("A")), Rcpp::as(P.field("b"))); - if (lp_solve) { - InnerBall = ComputeChebychevBall(HP.get_mat(), HP.get_vec()); - } else { - InnerBall = HP.ComputeInnerBall(); - } - if (InnerBall.second < 0.0) throw Rcpp::exception("Unable to compute a feasible point."); - break; - } - case 2: { - // Vpolytope - Vpolytope VP(n, Rcpp::as(P.field("V")), VT::Ones(Rcpp::as(P.field("V")).rows())); - InnerBall = VP.ComputeInnerBall(); - if (InnerBall.second < 0.0) throw Rcpp::exception("Unable to compute a feasible point."); - break; - } - case 3: { - // Zonotope - zonotope ZP(n, Rcpp::as(P.field("G")), VT::Ones(Rcpp::as(P.field("G")).rows())); - InnerBall = ZP.ComputeInnerBall(); - if (InnerBall.second < 0.0) throw Rcpp::exception("Unable to compute a feasible point."); - break; - } - case 4: { - // Intersection of two V-polytopes - Vpolytope VP1(n, Rcpp::as(P.field("V1")), VT::Ones(Rcpp::as(P.field("V1")).rows())); - Vpolytope VP2(n, Rcpp::as(P.field("V2")), VT::Ones(Rcpp::as(P.field("V2")).rows())); - InterVP VPcVP(VP1, VP2); - if (!VPcVP.is_feasible()) throw Rcpp::exception("Empty set!"); - InnerBall = VPcVP.ComputeInnerBall(); - if (InnerBall.second < 0.0) throw Rcpp::exception("Unable to compute a feasible point."); - break; - } - } - - Rcpp::NumericVector vec(n + 1); - for (unsigned int k = 0; k < n; ++k){ - vec[k] = InnerBall.first[k]; - } - - vec[n] = InnerBall.second; - return vec; -} diff --git a/R-proj/src/ode_solve.cpp b/R-proj/src/ode_solve.cpp deleted file mode 100644 index 07766d33a..000000000 --- a/R-proj/src/ode_solve.cpp +++ /dev/null @@ -1,205 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2020 Vissarion Fisikopoulos -// Copyright (c) 2018-2020 Apostolos Chalkis -// Copyright (c) 2020-2020 Marios Papachristou - -//Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2018 and 2019 program. - - -#include -#include -#include -#include -#include -#include -#include -#include "random_walks/random_walks.hpp" -#include "volume/volume_sequence_of_balls.hpp" -#include "volume/volume_cooling_gaussians.hpp" -#include "sampling/sampling.hpp" -#include "ode_solvers/ode_solvers.hpp" -#include "oracle_functors_rcpp.h" - -template < - typename Solver, - typename MT -> -void run_ode_solver( - Solver &solver, - unsigned int &order, - unsigned int &num_steps, - unsigned int &dimension, - Rcpp::List &results) { - - std::vector results_temp; - - for (unsigned int i = 0; i < order; i++) { - MT temp_result; - temp_result.resize(dimension, num_steps); - results_temp.push_back(temp_result); - } - - for (unsigned int i = 0; i < num_steps; i++) { - for (unsigned int j = 0; j < order; j++) { - results_temp[j].col(i) = solver.xs[j].getCoefficients(); - } - solver.step(i, true); - } - - for (unsigned int i = 0; i < order; i++) { - std::ostringstream stringStream; - stringStream << "x_" << i + 1; - std::string state_name = stringStream.str(); - - results.push_back(Rcpp::wrap(results_temp[i]), state_name.c_str()); - - } - -} - -//' Solve an ODE of the form dx^n / dt^n = F(x, t) -//' -//' @param n The number of steps. -//' @param step_size The step size. -//' @param order The ODE order (default is n = 1) -//' @param dimension The dimension of each derivative -//' @param initial_time The initial time -//' @param F The function oracle F(x, t) in the ODE. -//' @param method The method to be used -//' @param initial_conditions The initial conditions provided to the solver. Must be provided in a list with keys "x_1", ..., "x_n" and column vectors as values. The state "x_n" represents the (n-1)-th order derivative with respect to time -//' @param domains A list of n H-polytopes with keys "P_1", "P_2", ..., "P_n" that correspond to each derivative's domain -//' -//' @return A list which contains elements "x_1", ..., "x_n" representing each derivative results. Each "x_i" corresponds to a d x n matrix where each column represents a certain timestep of the solver. -//' -//' @examples -//' # Please visit the examples directory on examples demonstrating usage of the ODE solvers. -//' -//' @export -// [[Rcpp::export]] -Rcpp::List ode_solve(Rcpp::Nullable n, - Rcpp::Nullable step_size, - Rcpp::Nullable order, - Rcpp::Nullable dimension, - Rcpp::Nullable initial_time, - Rcpp::Function F, - Rcpp::Nullable method, - Rcpp::Nullable domains = R_NilValue, - Rcpp::Nullable initial_conditions = R_NilValue) -{ - - typedef double NT; - typedef Cartesian Kernel; - typedef BoostRandomNumberGenerator RNGType; - typedef typename Kernel::Point Point; - typedef HPolytope Hpolytope; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - typedef std::vector pts; - typedef std::vector hp_bounds; - typedef RcppFunctor::GradientFunctor func; - - unsigned int n_ = Rcpp::as(n); - NT step_size_ = Rcpp::as(step_size); - unsigned int order_ = Rcpp::as(order); - std::string method_ = Rcpp::as(method); - unsigned int dim = Rcpp::as(dimension); - NT initial_time_ = Rcpp::as(initial_time); - - // Create functors - RcppFunctor::parameters rcpp_functor_params(1, 1, order_); - - // Create C++ functor - func F_(rcpp_functor_params, F, false); - - // Initialize initial conditions - pts initial_conditions_; - VT temp_initial_condition; - - for (unsigned int i = 1; i <= order_; i++) { - std::ostringstream stringStream; - stringStream << "x_" << i; - std::string state_name = stringStream.str(); - - if (Rcpp::as(initial_conditions).containsElementNamed(state_name.c_str())) { - temp_initial_condition = Rcpp::as(Rcpp::as(initial_conditions)[state_name.c_str()]); - Point p(temp_initial_condition); - initial_conditions_.push_back(p); - } else { - Point p(dim); - initial_conditions_.push_back(p); - } - - } - - unsigned int i = 0; - unsigned int t = 0; - F_(i, initial_conditions_, t); - - hp_bounds domains_; - - // Initialize domains - // TODO Test and add other polytope types - for (unsigned int i = 0; i < order_; i++) { - std::ostringstream stringStream; - stringStream << "P_" << i + 1; - std::string domain_name = stringStream.str(); - - if (Rcpp::as(domains).containsElementNamed(domain_name.c_str())) { - - Hpolytope HP(dim, Rcpp::as( - Rcpp::as(Rcpp::as(domains) - [domain_name.c_str()]).field("A")), - Rcpp::as( - Rcpp::as(Rcpp::as(domains) - [domain_name.c_str()]).field("b")) - ); - - HP.normalize(); - - Hpolytope *HP_ref = &HP; - - if (!HP_ref->is_in(initial_conditions_[i])) { - std::ostringstream errStream; - errStream << "Initial condition out of bounds for state " << i + 1 << ".\n"; - throw Rcpp::exception(errStream.str().c_str()); - } - domains_.push_back(HP_ref); - } else { - domains_.push_back(NULL); - } - } - - Rcpp::List results; - - if (method_ == "euler") { - EulerODESolver euler_solver = - EulerODESolver - (initial_time_, step_size_, initial_conditions_, F_, domains_); - run_ode_solver, MT>(euler_solver, order_, n_, dim, results); - } else if (method_ == "leapfrog") { - if (order_ % 2 == 1) { - throw Rcpp::exception("Leapfrog is an even order solver."); - } - LeapfrogODESolver leapfrog_solver = - LeapfrogODESolver - (initial_time_, step_size_, initial_conditions_, F_, domains_); - run_ode_solver, MT>(leapfrog_solver, order_, n_, dim, results); - } else if (method_ == "runge_kutta") { - RKODESolver rk_solver = - RKODESolver - (initial_time_, step_size_, initial_conditions_, F_, domains_); - run_ode_solver, MT>(rk_solver, order_, n_, dim, results); - } else if (method_ == "richardson") { - RichardsonExtrapolationODESolver r_solver = - RichardsonExtrapolationODESolver - (initial_time_, step_size_, initial_conditions_, F_, domains_); - run_ode_solver, MT>(r_solver, order_, n_, dim, results); - } else { - throw Rcpp::exception("Unrecognized solver. Aborting."); - } - - return results; -} diff --git a/R-proj/src/oracle_functors_rcpp.h b/R-proj/src/oracle_functors_rcpp.h deleted file mode 100644 index 6929540ca..000000000 --- a/R-proj/src/oracle_functors_rcpp.h +++ /dev/null @@ -1,160 +0,0 @@ -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2020 Vissarion Fisikopoulos -// Copyright (c) 2018-2020 Apostolos Chalkis -// Copyright (c) 2020-2020 Marios Papachristou - -// Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2020 program. - -// Licensed under GNU LGPL.3, see LICENCE file - -#ifndef ODE_SOLVERS_ORACLE_FUNCTORS_RCPP_HPP -#define ODE_SOLVERS_ORACLE_FUNCTORS_RCPP_HPP - -enum ode_solvers { - no_solver, - leapfrog, - euler, - runge_kutta, - richardson, - collocation, - implicit_midpoint -}; - -// Holds Oracle Functor that wraps an R function via RCpp -// The R function is provided as an Rcpp::Function object -// The functor uses Rcpp::as and Rcpp::wrap to do the conversion, -// call the oracle, and convert the results back to C++ -struct RcppFunctor { - - template < - typename NT - > - struct parameters { - NT L; // Lipschitz constant of gradient - NT m; // Strong-convexity parameter - NT eta; // Step-size (if defined by user) - NT kappa; // Condition number - unsigned int order; // Order of ODE functor - - parameters( - NT L_, - NT m_, - NT eta_, - unsigned int order_=2 - ) : - L(L_), - m(m_), - eta(eta_), - kappa(L_ / m_), - order(order_) - {} - }; - - // Log-probability gradient functor - template - < - typename Point - > - struct GradientFunctor { - typedef typename Point::FT NT; - typedef typename Point::Coeff VT; - typedef std::vector pts; - - parameters params; - Rcpp::Function neg_grad_f; // Negative gradient as an Rcpp::Function - bool negate; - - GradientFunctor( - parameters params_, - Rcpp::Function neg_grad_f_, - bool negate_=true): - params(params_), - neg_grad_f(neg_grad_f_), - negate(negate_) - {}; - - // The index i represents the state vector index - Point operator() (unsigned int const& i, pts const& xs, NT const& t) const { - if (i == params.order - 1) { - // Convert point to Rcpp::NumericMatrix - - VT y = Rcpp::as(neg_grad_f(Rcpp::wrap(xs[0].getCoefficients()))); - - Point z(y); - - if (negate) z = (-1.0) * z; - - // Return result as Point - return z; - } else { - return xs[i + 1]; // returns derivative - } - } - - Point operator() (Point const& x) const { - VT y = Rcpp::as(neg_grad_f(Rcpp::wrap(x.getCoefficients()))); - - Point z(y); - - if (negate) z = (-1.0) * z; - - // Return result as Point - return z; - } - - }; - - // Negative log-probability functor - template - < - typename Point - > - struct FunctionFunctor { - typedef typename Point::FT NT; - typedef typename Point::Coeff VT; - - parameters params; - Rcpp::Function negative_logprob; - - FunctionFunctor( - parameters params_, - Rcpp::Function negative_logprob_) : - params(params_), - negative_logprob(negative_logprob_) - {}; - - NT operator() (Point const& x) const { - return Rcpp::as(negative_logprob(Rcpp::wrap(x.getCoefficients()))); - } - - }; - - // Log-probability hessian functor - template - < - typename Point - > - struct HessianFunctor { - typedef typename Point::FT NT; - typedef typename Point::Coeff VT; - - parameters params; - Rcpp::Function hessian; // Negative hessian as an Rcpp::Function - - HessianFunctor( - parameters params_, - Rcpp::Function hessian_) : - params(params_), - hessian(hessian_) - {}; - - Point operator() (Point const& x) const { - VT y= Rcpp::as(hessian(Rcpp::wrap(x.getCoefficients()))); - return Point(y); - } - - }; -}; - -#endif diff --git a/R-proj/src/poly_gen.cpp b/R-proj/src/poly_gen.cpp deleted file mode 100644 index 1ffa75d7f..000000000 --- a/R-proj/src/poly_gen.cpp +++ /dev/null @@ -1,114 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2018 Vissarion Fisikopoulos -// Copyright (c) 2018 Apostolos Chalkis - -//Contributed and/or modified by Apostolos Chalkis, as part of Google Summer of Code 2018 program. - - -#include -#include -#include -#include "cartesian_geom/cartesian_kernel.h" -#include -#include -#include -#include -#include "convex_bodies/hpolytope.h" -#include "convex_bodies/vpolytope.h" -#include "convex_bodies/zpolytope.h" -#include "generators/known_polytope_generators.h" -#include "generators/h_polytopes_generator.h" -#include "generators/v_polytopes_generators.h" -#include "generators/z_polytopes_generators.h" -#include "extractMatPoly.h" - -//' An internal Rccp function as a polytope generator -//' -//' @param kind_gen An integer to declare the type of the polytope. -//' @param Vpoly_gen A boolean parameter to declare if the requested polytope has to be in V-representation. -//' @param Zono_gen A boolean parameter to declare if the requested polytope has to be a zonotope. -//' @param dim_gen An integer to declare the dimension of the requested polytope. -//' @param m_gen An integer to declare the number of generators for the requested random zonotope or the number of vertices for a V-polytope. -//' @param seed Optional. A fixed seed for the random polytope generator. -//' -//' @keywords internal -//' -//' @return A numerical matrix describing the requested polytope -// [[Rcpp::export]] -Rcpp::NumericMatrix poly_gen (int kind_gen, bool Vpoly_gen, bool Zono_gen, int dim_gen, int m_gen, - Rcpp::Nullable seed = R_NilValue) { - - typedef double NT; - typedef Cartesian Kernel; - typedef typename Kernel::Point Point; - typedef boost::mt19937 RNGType; - typedef HPolytope Hpolytope; - typedef VPolytope Vpolytope; - typedef Zonotope zonotope; - - double seed_rcpp = (!seed.isNotNull()) ? std::numeric_limits::signaling_NaN() : Rcpp::as(seed); - - if (Zono_gen) { - switch (kind_gen) { - - case 1: - return extractMatPoly(gen_zonotope_uniform(dim_gen, m_gen, seed_rcpp)); - case 2: - return extractMatPoly(gen_zonotope_gaussian(dim_gen, m_gen, seed_rcpp)); - case 3: - return extractMatPoly(gen_zonotope_exponential(dim_gen, m_gen, seed_rcpp)); - - } - - } else if (Vpoly_gen) { - switch (kind_gen) { - - case 1: - return extractMatPoly(generate_cube(dim_gen, true)); - - case 2: - return extractMatPoly(generate_cross(dim_gen, true)); - - case 3: - return extractMatPoly(generate_simplex(dim_gen, true)); - - case 4: - return extractMatPoly(random_vpoly(dim_gen, m_gen, seed_rcpp)); - - case 5: - return extractMatPoly(random_vpoly_incube(dim_gen, m_gen, seed_rcpp)); - - } - } else { - switch (kind_gen) { - - case 1: - return extractMatPoly(generate_cube(dim_gen, false)); - - case 2: - return extractMatPoly(generate_cross(dim_gen, false)); - - case 3: - return extractMatPoly(generate_simplex(dim_gen, false)); - - case 4: - return extractMatPoly(generate_prod_simplex(dim_gen)); - - case 5: - return extractMatPoly(generate_skinny_cube(dim_gen)); - - case 6: - return extractMatPoly(random_hpoly(dim_gen, m_gen, seed_rcpp)); - - case 7: - return extractMatPoly(generate_birkhoff(dim_gen)); - - } - } - - throw Rcpp::exception("Wrong inputs!"); - -} diff --git a/R-proj/src/polytopes_modules.cpp b/R-proj/src/polytopes_modules.cpp deleted file mode 100644 index 443251fbb..000000000 --- a/R-proj/src/polytopes_modules.cpp +++ /dev/null @@ -1,240 +0,0 @@ -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2019 Vissarion Fisikopoulos -// Copyright (c) 2018-2019 Apostolos Chalkis - - -#include - -class Hpolytope { -public: - Hpolytope() {} - Hpolytope(Rcpp::NumericMatrix _A, Rcpp::NumericVector _b) : A(_A), b(_b), Aeq(Rcpp::NumericMatrix(0,0)), - beq(Rcpp::NumericVector(0)), vol(std::numeric_limits::signaling_NaN()), dimension(_A.ncol()), type(1) {} - Hpolytope(Rcpp::NumericMatrix _A, Rcpp::NumericVector _b, Rcpp::NumericMatrix _Aeq, Rcpp::NumericVector _beq) : - A(_A), b(_b), Aeq(_Aeq), beq(_beq), vol(std::numeric_limits::signaling_NaN()), dimension(_A.ncol()), type(1) {} - Hpolytope(Rcpp::NumericMatrix _A, Rcpp::NumericVector _b, double volume) : A(_A), b(_b), - Aeq(Rcpp::NumericMatrix(0,0)), beq(Rcpp::NumericVector(0)), vol(volume), dimension(_A.ncol()), type(1) {} - Hpolytope(Rcpp::NumericMatrix _A, Rcpp::NumericVector _b, Rcpp::NumericMatrix _Aeq, Rcpp::NumericVector _beq, - double volume) : A(_A), b(_b), Aeq(_Aeq), beq(_beq), vol(volume), dimension(_A.ncol()), type(1) {} - Rcpp::NumericMatrix A; - Rcpp::NumericVector b; - Rcpp::NumericMatrix Aeq; - Rcpp::NumericVector beq; - double vol; - unsigned int dimension; - int type; -}; - -class Vpolytope { -public: - Vpolytope() {} - Vpolytope(Rcpp::NumericMatrix _V) : V(_V), vol(std::numeric_limits::signaling_NaN()), dimension(_V.ncol()), type(2) {} - Vpolytope(Rcpp::NumericMatrix _V, double volume) : V(_V), vol(volume), dimension(_V.ncol()), type(2) {} - Rcpp::NumericMatrix V; - double vol; - unsigned int dimension; - int type; -}; - -class Zonotope { -public: - Zonotope() {} - Zonotope(Rcpp::NumericMatrix _G) : G(_G), vol(std::numeric_limits::signaling_NaN()), dimension(_G.ncol()), type(3) {} - Zonotope(Rcpp::NumericMatrix _G, double volume) : G(_G), vol(volume), dimension(_G.ncol()), type(3) {} - Rcpp::NumericMatrix G; - double vol; - unsigned int dimension; - int type; -}; - -class VPinterVP { -public: - VPinterVP() {} - VPinterVP(Rcpp::NumericMatrix _V1, Rcpp::NumericMatrix _V2) : V1(_V1), V2(_V2), vol(std::numeric_limits::signaling_NaN()), dimension(_V1.ncol()), type(4) {} - VPinterVP(Rcpp::NumericMatrix _V1, Rcpp::NumericMatrix _V2, double volume) : V1(_V1), V2(_V2), vol(volume), dimension(_V1.ncol()), type(4) {} - Rcpp::NumericMatrix V1; - Rcpp::NumericMatrix V2; - double vol; - unsigned int dimension; - int type; -}; -#include -using SpMat=Eigen::SparseMatrix ; -class sparse_constraint_problem { -public: - sparse_constraint_problem() {} - sparse_constraint_problem(SpMat _Aineq, Rcpp::NumericVector _bineq, SpMat _Aeq, Rcpp::NumericVector _beq) : Aineq(_Aineq), bineq(_bineq), Aeq(_Aeq), beq(_beq), - lb(Rcpp::NumericVector(_Aineq.cols(),-1e9)), ub(Rcpp::NumericMatrix(_Aineq.cols(),1e9)), dimension(_Aineq.cols()), type(5) {} - sparse_constraint_problem(SpMat _Aineq, Rcpp::NumericVector _bineq, SpMat _Aeq, Rcpp::NumericVector _beq, - Rcpp::NumericVector _lb, Rcpp::NumericVector _ub) : Aineq(_Aineq), bineq(_bineq), Aeq(_Aeq), beq(_beq), - lb(_lb), ub(_ub), dimension(_Aineq.cols()), type(5) {} - sparse_constraint_problem(SpMat _Aeq, Rcpp::NumericVector _beq) : Aineq(SpMat(0, _Aeq.cols())), bineq(Rcpp::NumericVector(0)), Aeq(_Aeq), beq(_beq), - lb(Rcpp::NumericVector(_Aeq.cols(),-1e9)), ub(Rcpp::NumericMatrix(_Aeq.cols(),1e9)), dimension(_Aeq.cols()), type(5) {} - SpMat Aineq; - Rcpp::NumericVector bineq; - SpMat Aeq; - Rcpp::NumericVector beq; - Rcpp::NumericVector lb; - Rcpp::NumericVector ub; - unsigned int dimension; - int type; -}; -RCPP_MODULE(polytopes){ - using namespace Rcpp ; - - //' An exposed class to represent a H-polytope - //' - //' @description A H-polytope is a convex polytope defined by a set of linear inequalities or equivalently a \eqn{d}-dimensional H-polytope with \eqn{m} facets is defined by a \eqn{m\times d} matrix A and a \eqn{m}-dimensional vector b, s.t.: \eqn{Ax\leq b}. - //' - //' @field A \eqn{m\times d} numerical matrix A - //' @field b \eqn{m}-dimensional vector b - //' @field Aeq \eqn{q\times d} numerical matrix Aeq - //' @field beq \eqn{q}-dimensional vector beq - //' @field volume The volume of the polytope. - //' @field dimension An integer that declares the dimension of the polytope. It has not be given to the constructor. - //' @field type An integer that declares the representation of the polytope. For H-representation the default value is 1. It has not be given to the constructor. - //' - //' @example - //' # create a 2-d unit simplex - //' A = matrix(c(-1,0,0,-1,1,1), ncol=2, nrow=3, byrow=TRUE) - //' b = c(0,0,1) - //' P = Hpolytope$new(A,b) - //' @export - class_("Hpolytope") - // expose the default constructor - .constructor() - .constructor() - .constructor() - .constructor() - .constructor() - - .field( "A", &Hpolytope::A ) - .field( "b", &Hpolytope::b ) - .field( "Aeq", &Hpolytope::Aeq ) - .field( "beq", &Hpolytope::beq ) - .field( "volume", &Hpolytope::vol ) - .field( "dimension", &Hpolytope::dimension ) - .field( "type", &Hpolytope::type ); - - //' An exposed C++ class to represent a V-polytope - //' - //' @description A V-polytope is a convex polytope defined by the set of its vertices. - //' - //' @field V \eqn{m\times d} numerical matrix that contains the vertices row-wise - //' @field volume The volume of the polytope. - //' @field dimension An integer that declares the dimension of the polytope. It has not be given to the constructor. - //' @field type An integer that declares the representation of the polytope. For H-representation the default value is 2. It has not be given to the constructor. - //' - //' @example - //' # Create a 2-d cube in V-representation - //' V = matrix(c(-1,1,1,1,1,-1,-1,-1), ncol=3, byrow=TRUE) - //' P = Vpolytope$new(V) - //' @export - class_("Vpolytope") - // expose the default constructor - .constructor() - .constructor() - .constructor() - - .field( "V", &Vpolytope::V ) - .field( "volume", &Vpolytope::vol ) - .field( "dimension", &Vpolytope::dimension ) - .field( "type", &Vpolytope::type ); - - //' An exposed C++ class to represent a zonotope - //' - //' @description A zonotope is a convex polytope defined by the Minkowski sum of \eqn{m} \eqn{d}-dimensional segments. - //' - //' @field G \eqn{m\times d} numerical matrix that contains the segments (or generators) row-wise - //' @field volume The volume of the polytope. - //' @field dimension An integer that declares the dimension of the polytope. It has not be given to the constructor. - //' @field type An integer that declares the representation of the polytope. For H-representation the default value is 3. It has not be given to the constructor. - //' - //' @example - //' # Create a 2-d zonotope with 4 generators - //' G = matrix(c(1,0,0,1,-0.73,0.67,-0.25,0.96), ncol = 2, nrow = 4, byrow = TRUE) - //' P = Zonotope$new(G) - //' @export - class_("Zonotope") - // expose the default constructor - .constructor() - .constructor() - .constructor() - - .field( "G", &Zonotope::G ) - .field( "volume", &Zonotope::vol ) - .field( "dimension", &Zonotope::dimension ) - .field( "type", &Zonotope::type ); - - //' An exposed C++ class to represent an intersection of two V-polytopes - //' - //' @description An intersection of two V-polytopes is defined by the intersection of the two coresponding convex hulls. - //' - //' @field V1 \eqn{m\times d} numerical matrix that contains the vertices of the first V-polytope (row-wise) - //' @field V2 \eqn{q\times d} numerical matrix that contains the vertices of the second V-polytope (row-wise) - //' @field volume The volume of the polytope. - //' @field dimension An integer that declares the dimension of the polytope. It has not be given to the constructor. - //' @field type An integer that declares the representation of the polytope. For H-representation the default value is 4. It has not be given to the constructor. - //' - //' @example - //' # define the intwrsection of a 2-d simplex with a 2-d cross polytope - //' P1 = gen_simplex(2,'V') - //' P2 = gen_cross(2,'V') - //' P = VpolytopeIntersection$new(P1$V, P2$V) - //' @export - class_("VpolytopeIntersection") - // expose the default constructor - .constructor() - .constructor() - .constructor() - - .field( "V1", &VPinterVP::V1 ) - .field( "V2", &VPinterVP::V2 ) - .field( "volume", &VPinterVP::vol ) - .field( "dimension", &VPinterVP::dimension ) - .field( "type", &VPinterVP::type ); - - //' An exposed class to represent a sparse constraint problem - //' - //' @description A constraint problem is defined by a set of linear inequalities and equalities or equivalently a \eqn{d}-dimensional constraint problem is defined by a \eqn{mineq\times d} matrix Aineq and a \eqn{mineq}-dimensional vector bineq, s.t.: \eqn{Aineqx\leq bineq}, a \eqn{meq\times d} matrix Aeq and a \eqn{meq}-dimensional vector beq, s.t.: \eqn{Aeqx\eq beq} and two \eqn{d} vectors lb, ub such that \eqn{lb\leq x \leq ub}. - //' - //' @field Aineq \eqn{mineq\times d} sparse matrix Aineq - //' @field bineq \eqn{mineq}-dimensional vector bineq - //' @field Aeq \eqn{meq\times d} sparse matrix Aeq - //' @field beq \eqn{meq}-dimensional vector beq - //' @field lb \eqn{d}-dimensional vector lb - //' @field ub \eqn{d}-dimensional vector ub - //' @field dimension An integer that declares the dimension of the polytope. It has not be given to the constructor. - //' @field type An integer that declares the representation of the polytope. For sparse_constraint_problem the default value is 5. It has not be given to the constructor. - //' - //' @example - //' # create a 2-d unit simplex - //' Aineq = matrix(, nrow=0, ncol=2, byrow = TRUE) - //' Aineq = as( Aineq, 'dgCMatrix' ) - //' bineq= matrix(,nrow=0, ncol=1, byrow=TRUE) - //' Aeq = matrix(c(1,1), ncol=2, nrow=1, byrow=TRUE) - //' Aeq = as( Aeq, 'dgCMatrix' ) - //' beq = c(1) - //' lb = c(0,0) - //' ub = c(1,1) - //' P = sparse_constraint_problem$new(Aineq, bineq, Aeq, beq, lb, ub) - //' @export - class_("sparse_constraint_problem") - // expose the default constructor - .constructor() - .constructor() - .constructor() - .constructor() - - .field( "Aineq", &sparse_constraint_problem::Aineq ) - .field( "bineq", &sparse_constraint_problem::bineq ) - .field( "Aeq", &sparse_constraint_problem::Aeq ) - .field( "beq", &sparse_constraint_problem::beq ) - .field( "lb", &sparse_constraint_problem::lb ) - .field( "ub", &sparse_constraint_problem::ub ) - .field( "dimension", &sparse_constraint_problem::dimension ) - .field( "type", &sparse_constraint_problem::type ); -} - -extern SEXP _rcpp_module_boot_polytopes(void); diff --git a/R-proj/src/psrf_multivariate.cpp b/R-proj/src/psrf_multivariate.cpp deleted file mode 100644 index d9392f3f8..000000000 --- a/R-proj/src/psrf_multivariate.cpp +++ /dev/null @@ -1,41 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 20014-2020 Vissarion Fisikopoulos -// Copyright (c) 2018-2020 Apostolos Chalkis - -//Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. - - -#include -#include -#include -#include -#include -#include -#include -#include "diagnostics/multivariate_psrf.hpp" - -//' Gelman-Rubin Potential Scale Reduction Factor (PSRF) -//' -//' @param samples A matrix that contans column-wise the sampled points from a geometric random walk. -//' -//' @references \cite{Gelman, A. and Rubin, D. B., -//' \dQuote{Inference from iterative simulation using multiple sequences,} \emph{Statistical Science,} 1992.} -//' -//' @references \cite{Brooks, S. and Gelman, A., -//' \dQuote{General Methods for Monitoring Convergence of Iterative Simulations,} \emph{Journal of Computational and Graphical Statistics,} 1998.} -//' -//' @return The value of multivariate PSRF by S. Brooks and A. Gelman. -//' -//' @export -// [[Rcpp::export]] -double psrf_multivariate(Rcpp::NumericMatrix samples) -{ - typedef double NT; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - - return multivariate_psrf(Rcpp::as(samples)); -} diff --git a/R-proj/src/psrf_univariate.cpp b/R-proj/src/psrf_univariate.cpp deleted file mode 100644 index f01edadaf..000000000 --- a/R-proj/src/psrf_univariate.cpp +++ /dev/null @@ -1,59 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 20014-2020 Vissarion Fisikopoulos -// Copyright (c) 2018-2020 Apostolos Chalkis - -//Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. - - -#include -#include -#include -#include -#include -#include -#include -#include "diagnostics/univariate_psrf.hpp" -#include "diagnostics/interval_psrf.hpp" - -//' Gelman-Rubin and Brooks-Gelman Potential Scale Reduction Factor (PSRF) for each marginal -//' -//' @param samples A matrix that contans column-wise the sampled points from a geometric random walk. -//' @param method A string to reauest diagnostic: (i) \code{'normal'} for psrf of Gelman-Rubin and (ii) \code{'interval'} for psrf of Brooks-Gelman. -//' -//' @references \cite{Gelman, A. and Rubin, D. B., -//' \dQuote{Inference from iterative simulation using multiple sequences,} \emph{Statistical Science,} 1992.} -//' -//' @references \cite{Brooks, S. and Gelman, A., -//' \dQuote{General Methods for Monitoring Convergence of Iterative Simulations,} \emph{Journal of Computational and Graphical Statistics,} 1998.} -//' -//' @return A vector that contains the values of PSRF for each coordinate -//' -//' @export -// [[Rcpp::export]] -Rcpp::NumericVector psrf_univariate(Rcpp::NumericMatrix samples, - Rcpp::Nullable method = R_NilValue) -{ - typedef double NT; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - - VT scores(samples.nrow()); - - std::string method_rcpp = std::string("normal"); - if(method.isNotNull()) { - method_rcpp = Rcpp::as(method); - } - - if (method_rcpp.compare(std::string("normal")) == 0) { - scores = univariate_psrf(Rcpp::as(samples)); - } else if(method_rcpp.compare(std::string("interval")) == 0) { - scores = interval_psrf(Rcpp::as(samples)); - } else { - throw Rcpp::exception("Unknown method!"); - } - - return Rcpp::wrap(scores); -} diff --git a/R-proj/src/raftery.cpp b/R-proj/src/raftery.cpp deleted file mode 100644 index e8e4f9ec8..000000000 --- a/R-proj/src/raftery.cpp +++ /dev/null @@ -1,51 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2020 Vissarion Fisikopoulos -// Copyright (c) 2020 Apostolos Chalkis - -//Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. - - -#include -#include -#include -#include "cartesian_geom/cartesian_kernel.h" -#include -#include -#include -#include -#include "diagnostics/raftery.hpp" - -//' Raftery and Lewis MCMC diagnostic -//' -//' @param samples A matrix that contans column-wise the sampled points from a geometric random walk. -//' @param q Optional. The quantile of the quantity of interest. The default value is 0.025. -//' @param r Optional. The level of precision desired. The default value is 0.01. -//' @param s Optional. The probability associated with r. The default value is 0.95. -//' -//' @references \cite{Raftery, A. E. and Lewis, S. M., -//' \dQuote{How many iterations in the Gibbs sampler?,} \emph{Bayesian Statistics 4. Proceedings of the Fourth Valencia International Meeting,} 1992.} -//' -//' @return (i) The number of draws required for burn-in, (ii) the skip parameter for 1st-order Markov chain, (iii) the skip parameter sufficient to get independence chain, (iv) the number of draws required to achieve r precision, (v) the number of draws if the chain is white noise, (vi) the I-statistic from Raftery and Lewis (1992). -//' -//' @export -// [[Rcpp::export]] -Rcpp::NumericMatrix raftery(Rcpp::NumericMatrix samples, - Rcpp::Nullable q = R_NilValue, - Rcpp::Nullable r = R_NilValue, - Rcpp::Nullable s = R_NilValue) -{ - typedef double NT; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - - NT _q = (!q.isNotNull()) ? 0.025 : Rcpp::as(q); - NT _r = (!r.isNotNull()) ? 0.01 : Rcpp::as(r); - NT _s = (!s.isNotNull()) ? 0.95 : Rcpp::as(s); - - MT results = perform_raftery(Rcpp::as(samples), _q, _r, _s); - - return Rcpp::wrap(results); -} diff --git a/R-proj/src/rotating.cpp b/R-proj/src/rotating.cpp deleted file mode 100644 index 607c00921..000000000 --- a/R-proj/src/rotating.cpp +++ /dev/null @@ -1,102 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2018 Vissarion Fisikopoulos -// Copyright (c) 2018 Apostolos Chalkis - -//Contributed and/or modified by Apostolos Chalkis, as part of Google Summer of Code 2018 program. - - -#include -#include -#include -#include "cartesian_geom/cartesian_kernel.h" -#include -#include -#include -#include -#include "volume/volume_sequence_of_balls.hpp" -#include "volume/rotating.hpp" -#include "extractMatPoly.h" - -//' An internal Rccp function for the random rotation of a convex polytope -//' -//' @param P A convex polytope (H-, V-polytope or a zonotope). -//' @param T Optional. A rotation matrix. -//' @param seed Optional. A fixed seed for the random linear map generator. -//' -//' @keywords internal -//' -//' @return A matrix that describes the rotated polytope -// [[Rcpp::export]] -Rcpp::NumericMatrix rotating (Rcpp::Reference P, Rcpp::Nullable T = R_NilValue, - Rcpp::Nullable seed = R_NilValue){ - - typedef double NT; - typedef Cartesian Kernel; - typedef typename Kernel::Point Point; - typedef BoostRandomNumberGenerator RNGType; - typedef HPolytope Hpolytope; - typedef VPolytope Vpolytope; - typedef Zonotope zonotope; - typedef IntersectionOfVpoly InterVP; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - - MT TransorfMat; - Rcpp::NumericMatrix Mat; - unsigned int n = P.field("dimension"), type = P.field("type"); - - int seed_rcpp = (!seed.isNotNull()) ? std::chrono::system_clock::now() - .time_since_epoch().count() - : Rcpp::as(seed); - - switch (type) { - case 1: { - // Hpolytope - Hpolytope HP(n, Rcpp::as(P.field("A")), Rcpp::as(P.field("b"))); - if (T.isNotNull()) { - TransorfMat = Rcpp::as(T); - HP.linear_transformIt(TransorfMat.inverse()); - } else { - TransorfMat = rotating < MT > (HP, seed_rcpp); - } - Mat = extractMatPoly(HP); - break; - } - case 2: { - // Vpolytope - Vpolytope VP(n, Rcpp::as(P.field("V")), VT::Ones(Rcpp::as(P.field("V")).rows())); - if (T.isNotNull()) { - TransorfMat = Rcpp::as(T); - VP.linear_transformIt(TransorfMat.inverse()); - } else { - TransorfMat = rotating < MT > (VP, seed_rcpp); - } - Mat = extractMatPoly(VP); - break; - } - case 3: { - // Zonotope - zonotope ZP(n, Rcpp::as(P.field("G")), VT::Ones(Rcpp::as(P.field("G")).rows())); - if (T.isNotNull()) { - TransorfMat = Rcpp::as(T); - ZP.linear_transformIt(TransorfMat.inverse()); - } else { - TransorfMat = rotating < MT > (ZP, seed_rcpp); - } - Mat = extractMatPoly(ZP); - break; - } - case 4: { - throw Rcpp::exception("volesti does not support rotation for this representation currently."); - } - } - - TransorfMat.conservativeResize(n+1, n); - TransorfMat.row(n) = VT::Ones(n); - MT res(TransorfMat.rows(), Rcpp::as(Mat).rows()+n); - res << Rcpp::as(Mat).transpose(), TransorfMat; - return Rcpp::wrap(res); -} diff --git a/R-proj/src/rounding.cpp b/R-proj/src/rounding.cpp deleted file mode 100644 index 0b2cbe0f0..000000000 --- a/R-proj/src/rounding.cpp +++ /dev/null @@ -1,142 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2020 Vissarion Fisikopoulos -// Copyright (c) 2018-2020 Apostolos Chalkis - -//Contributed and/or modified by Apostolos Chalkis, as part of Google Summer of Code 2018 program. -//Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. - - -#include -#include -#include -#include "cartesian_geom/cartesian_kernel.h" -#include -#include -#include -#include -#include "random_walks/random_walks.hpp" -#include "volume/volume_sequence_of_balls.hpp" -#include "volume/volume_cooling_gaussians.hpp" -#include "preprocess/min_sampling_covering_ellipsoid_rounding.hpp" -#include "preprocess/svd_rounding.hpp" -#include "preprocess/max_inscribed_ellipsoid_rounding.hpp" -#include "extractMatPoly.h" - -template -< - typename MT, - typename VT, - typename WalkType, - typename Polytope, - typename Point, - typename NT, - typename RNGType -> -std::tuple apply_rounding(Polytope &P, - std::string const& method_rcpp, - unsigned int const& walkL, - std::pair &InnerBall, - RNGType &rng) -{ - std::tuple round_res; - if (method_rcpp.compare(std::string("min_ellipsoid")) == 0) { - round_res = min_sampling_covering_ellipsoid_rounding(P, InnerBall, walkL, rng); - } else if (method_rcpp.compare(std::string("isotropy")) == 0) { - round_res = svd_rounding(P, InnerBall, walkL, rng); - } else { - throw Rcpp::exception("Unknown method!"); - } - return round_res; -} - -//' Internal rcpp function for the rounding of a convex polytope -//' -//' @param P A convex polytope (H- or V-representation or zonotope). -//' @param method Optional. The method to use for rounding, a) \code{'min_ellipsoid'} for the method based on mimimmum volume enclosing ellipsoid of a uniform sample from P, b) \code{'max_ellipsoid'} for the method based on maximum volume enclosed ellipsoid in P, (c) \code{'isotropy'} for the method based on isotropy. The default method is \code{'min_ellipsoid'} for all the representations. -//' @param seed Optional. A fixed seed for the number generator. -//' -//' @keywords internal -//' -//' @return A numerical matrix that describes the rounded polytope, a numerical matrix of the inverse linear transofmation that is applied on the input polytope, the numerical vector the the input polytope is shifted and the determinant of the matrix of the linear transformation that is applied on the input polytope. -// [[Rcpp::export]] -Rcpp::List rounding (Rcpp::Reference P, - Rcpp::Nullable method = R_NilValue, - Rcpp::Nullable seed = R_NilValue){ - - typedef double NT; - typedef Cartesian Kernel; - typedef typename Kernel::Point Point; - typedef BoostRandomNumberGenerator RNGType; - typedef HPolytope Hpolytope; - typedef VPolytope Vpolytope; - typedef Zonotope zonotope; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - - unsigned int n = P.field("dimension"), walkL = 2, type = P.field("type"); - std::string method_rcpp = std::string("isotropy"); - if(method.isNotNull()) { - method_rcpp = Rcpp::as(method); - if (method_rcpp.compare(std::string("max_ellipsoid")) == 0 && type != 1) { - Rcpp::exception("This method can not be used for V- or Z-polytopes!"); - } - } - - RNGType rng(n); - if (seed.isNotNull()) { - unsigned seed_rcpp = Rcpp::as(seed); - rng.set_seed(seed_rcpp); - } - - std::pair InnerBall; - Rcpp::NumericMatrix Mat; - - std::tuple round_res; - switch (type) { - case 1: { - // Hpolytope - - if (Rcpp::as(P.field("Aeq")).rows() > 0) { - throw Rcpp::exception("volesti supports rounding for full dimensional polytopes"); - } - Hpolytope HP(n, Rcpp::as(P.field("A")), Rcpp::as(P.field("b"))); - InnerBall = HP.ComputeInnerBall(); - if (InnerBall.second < 0.0) throw Rcpp::exception("Unable to compute a feasible point."); - if (method_rcpp.compare(std::string("max_ellipsoid")) == 0) { - round_res = max_inscribed_ellipsoid_rounding(HP, InnerBall.first); - } else { - round_res = apply_rounding(HP, method_rcpp, walkL, InnerBall, rng); - } - Mat = extractMatPoly(HP); - break; - } - case 2: { - // Vpolytope - Vpolytope VP(n, Rcpp::as(P.field("V")), VT::Ones(Rcpp::as(P.field("V")).rows())); - InnerBall = VP.ComputeInnerBall(); - if (InnerBall.second < 0.0) throw Rcpp::exception("Unable to compute a feasible point."); - round_res = apply_rounding(VP, method_rcpp, walkL, InnerBall, rng); - Mat = extractMatPoly(VP); - break; - } - case 3: { - // Zonotope - zonotope ZP(n, Rcpp::as(P.field("G")), VT::Ones(Rcpp::as(P.field("G")).rows())); - InnerBall = ZP.ComputeInnerBall(); - if (InnerBall.second < 0.0) throw Rcpp::exception("Unable to compute a feasible point."); - round_res = apply_rounding(ZP, method_rcpp, walkL, InnerBall, rng); - Mat = extractMatPoly(ZP); - break; - } - case 4: { - throw Rcpp::exception("volesti does not support rounding for this representation currently."); - } - } - - return Rcpp::List::create(Rcpp::Named("Mat") = Mat, Rcpp::Named("T") = Rcpp::wrap(std::get<0>(round_res)), - Rcpp::Named("shift") = Rcpp::wrap(std::get<1>(round_res)), - Rcpp::Named("round_value") = std::get<2>(round_res)); -} diff --git a/R-proj/src/sample_points.cpp b/R-proj/src/sample_points.cpp deleted file mode 100644 index 1014e74ea..000000000 --- a/R-proj/src/sample_points.cpp +++ /dev/null @@ -1,795 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2021 Vissarion Fisikopoulos -// Copyright (c) 2018-2021 Apostolos Chalkis - -//Contributed and/or modified by Apostolos Chalkis, as part of Google Summer of Code 2018 and 2019 program. - -// Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. - - -#include -#include -#include -#include -#include -#include -#include -#include "random_walks/random_walks.hpp" -#include "volume/volume_sequence_of_balls.hpp" -#include "volume/volume_cooling_gaussians.hpp" -#include "sampling/sampling.hpp" -#include "ode_solvers/ode_solvers.hpp" -#include "oracle_functors_rcpp.h" -#include "preprocess/crhmc/constraint_problem.h" -enum random_walks { - ball_walk, - rdhr, - cdhr, - billiard, - accelarated_billiard, - dikin_walk, - vaidya_walk, - john_walk, - brdhr, - bcdhr, - hmc, - nuts, - gaussian_hmc, - exponential_hmc, - uld, - crhmc -}; - -template < - typename Polytope, - typename RNGType, - typename PointList, - typename NT, - typename Point, - typename NegativeGradientFunctor, - typename NegativeLogprobFunctor, - typename HessianFunctor -> -void sample_from_polytope(Polytope &P, int type, RNGType &rng, PointList &randPoints, - unsigned int const& walkL, unsigned int const& numpoints, - bool const& gaussian, NT const& a, NT const& L, Point const& c, - Point const& StartingPoint, unsigned int const& nburns, - bool const& set_L, NT const& eta, random_walks walk, - NegativeGradientFunctor *F=NULL, NegativeLogprobFunctor *f=NULL, - HessianFunctor *h=NULL, ode_solvers solver_type = no_solver) -{ - switch (walk) - { - case bcdhr: - uniform_sampling_boundary (randPoints, P, rng, walkL, numpoints, - StartingPoint, nburns); - break; - case brdhr: - uniform_sampling_boundary (randPoints, P, rng, walkL, numpoints, - StartingPoint, nburns); - break; - case cdhr: - if(gaussian) { - gaussian_sampling(randPoints, P, rng, walkL, numpoints, - a, StartingPoint, nburns); - } else { - uniform_sampling(randPoints, P, rng, walkL, numpoints, - StartingPoint, nburns); - } - break; - case rdhr: - if(gaussian) { - gaussian_sampling(randPoints, P, rng, walkL, numpoints, - a, StartingPoint, nburns); - } else { - uniform_sampling(randPoints, P, rng, walkL, numpoints, - StartingPoint, nburns); - } - break; - case gaussian_hmc: - if(set_L) { - GaussianHamiltonianMonteCarloExactWalk WalkType(L); - gaussian_sampling(randPoints, P, rng, WalkType, walkL, numpoints, a, StartingPoint, nburns); - } else { - gaussian_sampling(randPoints, P, rng, walkL, numpoints, a, - StartingPoint, nburns); - } - break; - case vaidya_walk: - if (set_L) { - VaidyaWalk WalkType(L); - uniform_sampling(randPoints, P, rng, WalkType, walkL, numpoints, StartingPoint, nburns); - } else { - uniform_sampling(randPoints, P, rng, walkL, numpoints, - StartingPoint, nburns); - } - break; - case dikin_walk: - if (set_L) { - DikinWalk WalkType(L); - uniform_sampling(randPoints, P, rng, WalkType, walkL, numpoints, StartingPoint, nburns); - } else { - uniform_sampling(randPoints, P, rng, walkL, numpoints, - StartingPoint, nburns); - } - break; - case john_walk: - if (set_L) { - JohnWalk WalkType(L); - uniform_sampling(randPoints, P, rng, WalkType, walkL, numpoints, StartingPoint, nburns); - } else { - uniform_sampling(randPoints, P, rng, walkL, numpoints, - StartingPoint, nburns); - } - break; - case billiard: - if(set_L) { - BilliardWalk WalkType(L); - uniform_sampling(randPoints, P, rng, WalkType, walkL, numpoints, StartingPoint, nburns); - } else { - uniform_sampling(randPoints, P, rng, walkL, numpoints, - StartingPoint, nburns); - } - break; - case accelarated_billiard: - if(set_L) { - AcceleratedBilliardWalk WalkType(L); - uniform_sampling(randPoints, P, rng, WalkType, walkL, numpoints, StartingPoint, nburns); - } else { - uniform_sampling(randPoints, P, rng, walkL, numpoints, - StartingPoint, nburns); - } - break; - case exponential_hmc: - if (set_L) { - ExponentialHamiltonianMonteCarloExactWalk WalkType(L); - exponential_sampling(randPoints, P, rng, WalkType, walkL, numpoints, c, a, StartingPoint, nburns); - } else { - exponential_sampling(randPoints, P, rng, walkL, numpoints, c, a, - StartingPoint, nburns); - } - break; - case ball_walk: - if (set_L) { - if (gaussian) { - GaussianBallWalk WalkType(L); - gaussian_sampling(randPoints, P, rng, WalkType, walkL, numpoints, a, - StartingPoint, nburns); - } else { - BallWalk WalkType(L); - uniform_sampling(randPoints, P, rng, WalkType, walkL, numpoints, - StartingPoint, nburns); - } - } else { - if(gaussian) { - gaussian_sampling(randPoints, P, rng, walkL, numpoints, - a, StartingPoint, nburns); - } else { - uniform_sampling(randPoints, P, rng, walkL, numpoints, - StartingPoint, nburns); - } - } - break; - case hmc: - switch (solver_type) { - case leapfrog: - logconcave_sampling < - PointList, - Polytope, - RNGType, - HamiltonianMonteCarloWalk, - NT, - Point, - NegativeGradientFunctor, - NegativeLogprobFunctor, - LeapfrogODESolver < - Point, - NT, - Polytope, - NegativeGradientFunctor - > - >(randPoints, P, rng, walkL, numpoints, StartingPoint, nburns, *F, *f); - break; - case euler: - logconcave_sampling < - PointList, - Polytope, - RNGType, - HamiltonianMonteCarloWalk, - NT, - Point, - NegativeGradientFunctor, - NegativeLogprobFunctor, - EulerODESolver < - Point, - NT, - Polytope, - NegativeGradientFunctor - > - >(randPoints, P, rng, walkL, numpoints, StartingPoint, nburns, *F, *f); - break; - default: - break; - } - - break; - case crhmc:{ - execute_crhmc(P, rng, randPoints, walkL, numpoints, nburns, F, f, h); - break; - } - case nuts: - - logconcave_sampling < - PointList, - Polytope, - RNGType, - NutsHamiltonianMonteCarloWalk, - NT, - Point, - NegativeGradientFunctor, - NegativeLogprobFunctor, - LeapfrogODESolver < - Point, - NT, - Polytope, - NegativeGradientFunctor - > - >(randPoints, P, rng, walkL, numpoints, StartingPoint, nburns, *F, *f); - - break; - case uld: - - logconcave_sampling < - PointList, - Polytope, - RNGType, - UnderdampedLangevinWalk, - NT, - Point, - NegativeGradientFunctor, - NegativeLogprobFunctor, - LeapfrogODESolver < - Point, - NT, - Polytope, - NegativeGradientFunctor - > - >(randPoints, P, rng, walkL, numpoints, StartingPoint, nburns, *F, *f); - - break; - default: - throw Rcpp::exception("Unknown random walk!"); - } -} - -//' Sample uniformly, normally distributed, or logconcave distributed points from a convex Polytope (H-polytope, V-polytope, zonotope or intersection of two V-polytopes). -//' -//' @param P A convex polytope. It is an object from class (a) Hpolytope or (b) Vpolytope or (c) Zonotope or (d) VpolytopeIntersection. -//' @param n The number of points that the function is going to sample from the convex polytope. -//' @param random_walk Optional. A list that declares the random walk and some related parameters as follows: -//' \itemize{ -//' \item{\code{walk} }{ A string to declare the random walk: i) \code{'CDHR'} for Coordinate Directions Hit-and-Run, -//' ii) \code{'RDHR'} for Random Directions Hit-and-Run, iii) \code{'BaW'} for Ball Walk, iv) \code{'BiW'} for Billiard walk, -//' v) \code{'dikin'} for dikin walk, vi) \code{'vaidya'} for vaidya walk, vii) \code{'john'} for john walk, -//' viii) \code{'BCDHR'} boundary sampling by keeping the extreme points of CDHR or ix) \code{'BRDHR'} boundary sampling by keeping the extreme points of RDHR, -//' x) \code{'NUTS'} for NUTS Hamiltonian Monte Carlo sampler (logconcave densities), xi) \code{'HMC'} for Hamiltonian Monte Carlo (logconcave densities), -//' xii) CRHMC for Riemannian HMC with H-polytope constraints (uniform and general logconcave densities), -//' xiii) \code{'ULD'} for Underdamped Langevin Dynamics using the Randomized Midpoint Method (logconcave densities), -//' xiii) \code{'ExactHMC'} for exact Hamiltonian Monte Carlo with reflections (spherical Gaussian or exponential distribution). -//' The default walk is \code{'aBiW'} for the uniform distribution, \code{'CDHR'} for the Gaussian distribution and H-polytopes and -//' \code{'BiW'} or \code{'RDHR'} for the same distributions and V-polytopes and zonotopes. \code{'NUTS'} is the default sampler for logconcave densities and \code{'CRHMC'} -//' for logconcave densities with H-polytope and sparse constrainted problems.} -//' \item{\code{walk_length} }{ The number of the steps per generated point for the random walk. The default value is \eqn{1}.} -//' \item{\code{nburns} }{ The number of points to burn before start sampling. The default value is \eqn{1}.} -//' \item{\code{starting_point} }{ A \eqn{d}-dimensional numerical vector that declares a starting point in the interior of the polytope for the random walk. The default choice is the center of the ball as that one computed by the function \code{inner_ball()}.} -//' \item{\code{BaW_rad} }{ The radius for the ball walk.} -//' \item{\code{L} }{ The maximum length of the billiard trajectory or the radius for the step of dikin, vaidya or john walk.} -//' \item{\code{solver} }{ Specify ODE solver for logconcave sampling. Options are i) leapfrog, ii) euler iii) runge-kutta iv) richardson} -//' \item{\code{step_size }{ Optionally chosen step size for logconcave sampling. Defaults to a theoretical value if not provided.} -//' } -//' @param distribution Optional. A list that declares the target density and some related parameters as follows: -//' \itemize{ -//' \item{\code{density} }{ A string: (a) \code{'uniform'} for the uniform distribution or b) \code{'gaussian'} for the multidimensional spherical distribution c) \code{logconcave} with form proportional to exp(-f(x)) where f(x) is L-smooth and m-strongly-convex d) \code{'exponential'} for the exponential distribution. The default target distribution is the uniform distribution.} -//' \item{\code{variance} }{ The variance of the multidimensional spherical gaussian or the exponential distribution. The default value is 1.} -//' \item{\code{mode} }{ A \eqn{d}-dimensional numerical vector that declares the mode of the Gaussian distribution. The default choice is the center of the as that one computed by the function \code{inner_ball()}.} -//' \item{\code{bias} }{ The bias vector for the exponential distribution. The default vector is \eqn{c_1 = 1} and \eqn{c_i = 0} for \eqn{i \neq 1}.} -//' \item{\code{L_} }{ Smoothness constant (for logconcave). } -//' \item{\code{m} }{ Strong-convexity constant (for logconcave). } -//' \item{\code{negative_logprob} }{ Negative log-probability (for logconcave). } -//' \item{\code{negative_logprob_gradient} }{ Negative log-probability gradient (for logconcave). } -//' } -//' @param seed Optional. A fixed seed for the number generator. -//' -//' @references \cite{Robert L. Smith, -//' \dQuote{Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed Over Bounded Regions,} \emph{Operations Research,} 1984.}, -//' -//' @references \cite{B.T. Polyak, E.N. Gryazina, -//' \dQuote{Billiard walk - a new sampling algorithm for control and optimization,} \emph{IFAC Proceedings Volumes,} 2014.}, -//' -//' @references \cite{Y. Chen, R. Dwivedi, M. J. Wainwright and B. Yu, -//' \dQuote{Fast MCMC Sampling Algorithms on Polytopes,} \emph{Journal of Machine Learning Research,} 2018.} -//' -//' @references \cite{Lee, Yin Tat, Ruoqi Shen, and Kevin Tian, -//' \dQuote{"Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo,"} \emph{arXiv preprint arXiv:2002.04121}, 2020.} -//' -//' @references \cite{Shen, Ruoqi, and Yin Tat Lee, -//' \dQuote{"The randomized midpoint method for log-concave sampling.",} \emph{Advances in Neural Information Processing Systems}, 2019.} -//' -//' @references \cite{Augustin Chevallier, Sylvain Pion, Frederic Cazals, -//' \dQuote{"Hamiltonian Monte Carlo with boundary reflections, and application to polytope volume calculations,"} \emph{Research Report preprint hal-01919855}, 2018.} -//' -//' @return A \eqn{d\times n} matrix that contains, column-wise, the sampled points from the convex polytope P. -//' @examples -//' # uniform distribution from the 3d unit cube in H-representation using ball walk -//' P = gen_cube(3, 'H') -//' points = sample_points(P, n = 100, random_walk = list("walk" = "BaW", "walk_length" = 5)) -//' -//' # gaussian distribution from the 2d unit simplex in H-representation with variance = 2 -//' A = matrix(c(-1,0,0,-1,1,1), ncol=2, nrow=3, byrow=TRUE) -//' b = c(0,0,1) -//' P = Hpolytope$new(A,b) -//' points = sample_points(P, n = 100, distribution = list("density" = "gaussian", "variance" = 2)) -//' -//' # uniform points from the boundary of a 2-dimensional random H-polytope -//' P = gen_rand_hpoly(2,20) -//' points = sample_points(P, n = 100, random_walk = list("walk" = "BRDHR")) -//' -//' # For sampling from logconcave densities see the examples directory -//' -//' @export -// [[Rcpp::export]] -Rcpp::NumericMatrix sample_points(Rcpp::Nullable P, - Rcpp::Nullable n, - Rcpp::Nullable random_walk = R_NilValue, - Rcpp::Nullable distribution = R_NilValue, - Rcpp::Nullable seed = R_NilValue){ - - typedef double NT; - typedef Cartesian Kernel; - typedef BoostRandomNumberGenerator RNGType; - typedef typename Kernel::Point Point; - typedef HPolytope Hpolytope; - typedef VPolytope Vpolytope; - typedef Zonotope zonotope; - typedef IntersectionOfVpoly InterVP; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - typedef Eigen::SparseMatrix SpMat; - typedef constraint_problem sparse_problem; - - unsigned int type = Rcpp::as(P).field("type"), dim = Rcpp::as(P).field("dimension"), - walkL = 1, numpoints, nburns = 0; - - RcppFunctor::GradientFunctor *F = NULL; - RcppFunctor::FunctionFunctor *f = NULL; - RcppFunctor::HessianFunctor *h = NULL; - - GaussianFunctor::GradientFunctor *G = NULL; - GaussianFunctor::FunctionFunctor *g = NULL; - GaussianFunctor::HessianFunctor *hess_g = NULL; - bool functor_defined = true; - - - RNGType rng(dim); - if (seed.isNotNull()) { - unsigned seed_rcpp = Rcpp::as(seed); - rng.set_seed(seed_rcpp); - } - - Point c(dim); - - NT radius = 1.0, L; - bool set_mode = false, gaussian = false, logconcave = false, exponential = false, - set_starting_point = false, set_L = false; - - random_walks walk; - ode_solvers solver; // Used only for logconcave sampling - - NT eta = 1; - std::list randPoints; - std::pair InnerBall; - Point mode(dim); - - numpoints = Rcpp::as(n); - if (numpoints <= 0) throw Rcpp::exception("The number of samples has to be a positive integer!"); - - if (!distribution.isNotNull() || !Rcpp::as(distribution).containsElementNamed("density")) { - walk = billiard; - } else if ( - Rcpp::as(Rcpp::as(distribution)["density"]).compare(std::string("uniform")) == 0) { - walk = billiard; - } else if ( - Rcpp::as(Rcpp::as(distribution)["density"]).compare(std::string("gaussian")) == 0) { - gaussian = true; - } else if ( - Rcpp::as(Rcpp::as(distribution)["density"]).compare(std::string("exponential")) == 0) { - walk = exponential_hmc; - exponential = true; - } else if ( - Rcpp::as(Rcpp::as(distribution)["density"]).compare(std::string("logconcave")) == 0) { - logconcave = true; - } else { - throw Rcpp::exception("Wrong distribution!"); - } - - if (Rcpp::as(distribution).containsElementNamed("mode")) { - if (!(gaussian || logconcave)) throw Rcpp::exception("Mode is given only for Gaussian/logconcave sampling!"); - if (Rcpp::as(Rcpp::as(distribution)["mode"]).size() != dim) { - throw Rcpp::exception("Mode has to be a point in the same dimension as the polytope P"); - } else { - set_mode = true; - VT temp = Rcpp::as(Rcpp::as(distribution)["mode"]); - mode = Point(dim, std::vector(&temp[0], temp.data() + temp.cols() * temp.rows())); - } - } - - NT a = 0.5; - if (Rcpp::as(distribution).containsElementNamed("variance")) { - a = 1.0 / (2.0 * Rcpp::as(Rcpp::as(distribution)["variance"])); - if (exponential) a = Rcpp::as(Rcpp::as(distribution)["variance"]); - if (!(gaussian || logconcave || exponential)) { - Rcpp::warning("The variance can be set only for Gaussian and exponential sampling!"); - } else if (a <= 0.0) { - throw Rcpp::exception("The variance has to be positive!"); - } - } - - if (Rcpp::as(distribution).containsElementNamed("bias")) { - c = Point(Rcpp::as(Rcpp::as(distribution)["bias"])); - } - - if (Rcpp::as(distribution).containsElementNamed("negative_logprob") && - Rcpp::as(distribution).containsElementNamed("negative_logprob_gradient")) { - - if (!logconcave) { - throw Rcpp::exception("The negative logprob and its gradient can be set only for logconcave sampling!"); - } - - // Parse arguments - Rcpp::Function negative_logprob = Rcpp::as(distribution)["negative_logprob"]; - Rcpp::Function negative_logprob_gradient = Rcpp::as(distribution)["negative_logprob_gradient"]; - - NT L_ = 1, m = 1; - - if (Rcpp::as(distribution).containsElementNamed("L_")) { - L_ = Rcpp::as(Rcpp::as(distribution)["L_"]); - if (L_ <= NT(0)) { - throw Rcpp::exception("The smoothness constant must be positive"); - } - } else { - L_ = -1; - } - - if (Rcpp::as(distribution).containsElementNamed("m")) { - m = Rcpp::as(Rcpp::as(distribution)["m"]); - if (m <= NT(0)) { - throw Rcpp::exception("The strong-convexity constant must be positive"); - } - } else { - m = -1; - } - - if (Rcpp::as(random_walk).containsElementNamed("step_size")) { - eta = NT(Rcpp::as(Rcpp::as(random_walk)["step_size"])); - if (eta <= NT(0)) { - throw Rcpp::exception("Step size must be positive"); - } - } else { - eta = NT(-1); - } - - if (Rcpp::as(random_walk).containsElementNamed("solver")) { - std::string solver_str = Rcpp::as(Rcpp::as(random_walk)["solver"]); - if (solver_str == "leapfrog") { - solver = leapfrog; - } else if (solver_str == "euler") { - solver = euler; - } else if (solver_str == "implicit_midpoint"){ - solver = implicit_midpoint; - } else { - throw Rcpp::exception("Invalid ODE solver specified. Aborting."); - } - } else { - solver = leapfrog; - } - // Create functors - RcppFunctor::parameters rcpp_functor_params(L_, m, eta, 2); - F = new RcppFunctor::GradientFunctor(rcpp_functor_params, negative_logprob_gradient); - f = new RcppFunctor::FunctionFunctor(rcpp_functor_params, negative_logprob); - if(Rcpp::as(distribution).containsElementNamed("negative_logprob_hessian")){ - Rcpp::Function negative_logprob_hessian = Rcpp::as(distribution)["negative_logprob_hessian"]; - h = new RcppFunctor::HessianFunctor(rcpp_functor_params, negative_logprob_hessian); - } - } - - else if (logconcave && !Rcpp::as(distribution).containsElementNamed("negative_logprob") && - !Rcpp::as(distribution).containsElementNamed("negative_logprob_gradient")) { - - functor_defined = false; - - if (Rcpp::as(random_walk).containsElementNamed("step_size")) { - eta = NT(Rcpp::as(Rcpp::as(random_walk)["step_size"])); - if (eta <= NT(0)) { - throw Rcpp::exception("Step size must be positive"); - } - } else { - eta = NT(-1); - } - - if (Rcpp::as(random_walk).containsElementNamed("solver")) { - std::string solver_str = Rcpp::as(Rcpp::as(random_walk)["solver"]); - if (solver_str == "leapfrog") { - solver = leapfrog; - } else if (solver_str == "euler") { - solver = euler; - } else if (solver_str == "implicit_midpoint"){ - solver = implicit_midpoint; - } else { - throw Rcpp::exception("Invalid ODE solver specified. Aborting."); - } - } else { - Rcpp::warning("Solver set to leapfrog."); - solver = leapfrog; - } - // Create functors - GaussianFunctor::parameters* gaussian_functor_params=new GaussianFunctor::parameters(mode, a, eta); - G = new GaussianFunctor::GradientFunctor(*gaussian_functor_params); - g = new GaussianFunctor::FunctionFunctor(*gaussian_functor_params); - hess_g = new GaussianFunctor::HessianFunctor(*gaussian_functor_params); - } - - if (!random_walk.isNotNull() || !Rcpp::as(random_walk).containsElementNamed("walk")) { - if (exponential) { - if (type !=1) { - throw Rcpp::exception("Exponential sampling is supported only for H-polytopes"); - } - walk = exponential_hmc; - } else if (logconcave) { - walk = (type == 5) ? crhmc : nuts; - } else if (gaussian) { - walk = (type == 1) ? cdhr : rdhr; - } else { - walk = (type == 1) ? accelarated_billiard : billiard; - } - } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("CDHR")) == 0) { - walk = cdhr; - } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("dikin")) == 0) { - walk = dikin_walk; - if (Rcpp::as(random_walk).containsElementNamed("L")) { - L = Rcpp::as(Rcpp::as(random_walk)["L"]); - set_L = true; - if (L <= 0.0) throw Rcpp::exception("L must be a postitive number!"); - } - } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("vaidya")) == 0) { - walk = vaidya_walk; - if (Rcpp::as(random_walk).containsElementNamed("L")) { - L = Rcpp::as(Rcpp::as(random_walk)["L"]); - set_L = true; - if (L <= 0.0) throw Rcpp::exception("L must be a postitive number!"); - } - } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("john")) == 0) { - walk = john_walk; - if (Rcpp::as(random_walk).containsElementNamed("L")) { - L = Rcpp::as(Rcpp::as(random_walk)["L"]); - set_L = true; - if (L <= 0.0) throw Rcpp::exception("L must be a postitive number!"); - } - } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("RDHR")) == 0) { - walk = rdhr; - } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("BaW")) == 0) { - walk = ball_walk; - if (Rcpp::as(random_walk).containsElementNamed("BaW_rad")) { - L = Rcpp::as(Rcpp::as(random_walk)["BaW_rad"]); - set_L = true; - if (L <= 0.0) throw Rcpp::exception("BaW diameter must be a postitive number!"); - } - } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("BiW")) == 0) { - if (gaussian) throw Rcpp::exception("Billiard walk can be used only for uniform sampling!"); - walk = billiard; - if (Rcpp::as(random_walk).containsElementNamed("L")) { - L = Rcpp::as(Rcpp::as(random_walk)["L"]); - set_L = true; - if (L <= 0.0) throw Rcpp::exception("L must be a postitive number!"); - } - } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("aBiW")) == 0) { - if (gaussian) throw Rcpp::exception("Billiard walk can be used only for uniform sampling!"); - walk = accelarated_billiard; - if (Rcpp::as(random_walk).containsElementNamed("L")) { - L = Rcpp::as(Rcpp::as(random_walk)["L"]); - set_L = true; - if (L <= 0.0) throw Rcpp::exception("L must be a postitive number!"); - } - } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("BRDHR")) == 0) { - if (gaussian || exponential) throw Rcpp::exception("Gaussian sampling from the boundary is not supported!"); - walk = brdhr; - } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("BCDHR")) == 0) { - if (gaussian) throw Rcpp::exception("Gaussian sampling from the boundary is not supported!"); - walk = bcdhr; - } else if(Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("ExactHMC")) == 0) { - if (!exponential && !gaussian) throw Rcpp::exception("Exact HMC is supported only for exponential or spherical Gaussian sampling."); - if(exponential){ - walk = exponential_hmc; - } else { - walk = gaussian_hmc; - } - } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("HMC")) == 0) { - if (!logconcave) throw Rcpp::exception("HMC is not supported for non first-order sampling"); - if (F->params.L < 0) throw Rcpp::exception("The smoothness constant is absent"); - if (F->params.m < 0) throw Rcpp::exception("The strong-convexity constant is absent"); - walk = hmc; - } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("NUTS")) == 0) { - if (!logconcave) throw Rcpp::exception("NUTS is not supported for non first-order sampling"); - walk = nuts; - } else if (Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("ULD")) == 0) { - if (!logconcave) throw Rcpp::exception("ULD is not supported for non first-order sampling"); - walk = uld; - } else if(Rcpp::as(Rcpp::as(random_walk)["walk"]).compare(std::string("CRHMC")) == 0){ - if (!logconcave) throw Rcpp::exception("CRHMC is used for logconcave sampling"); - if (type !=1 && type !=5 ) { - throw Rcpp::exception("CRHMC sampling is supported only for H-polytopes and Sparse Problems."); - } - walk =crhmc; - if(solver!=implicit_midpoint){ - Rcpp::warning("Solver set to implicit midpoint."); - } - solver = implicit_midpoint; - } else { - throw Rcpp::exception("Unknown walk type!"); - } - - Point StartingPoint; - if (Rcpp::as(random_walk).containsElementNamed("starting_point")) { - if (Rcpp::as(Rcpp::as(random_walk)["starting_point"]).size() != dim) { - throw Rcpp::exception("Starting Point has to lie in the same dimension as the polytope P"); - } else { - set_starting_point = true; - VT temp = Rcpp::as(Rcpp::as(random_walk)["starting_point"]); - StartingPoint = Point(dim, std::vector(&temp[0], temp.data() + temp.cols() * temp.rows())); - } - } - - if (Rcpp::as(random_walk).containsElementNamed("walk_length")) { - walkL = Rcpp::as(Rcpp::as(random_walk)["walk_length"]); - if (walkL <= 0) { - throw Rcpp::exception("The walk length has to be a positive integer!"); - } - } - - if (Rcpp::as(random_walk).containsElementNamed("nburns")) { - nburns = Rcpp::as(Rcpp::as(random_walk)["nburns"]); - if (nburns < 0) { - throw Rcpp::exception("The number of points to burn before sampling has to be a positive integer!"); - } - } - - switch(type) { - case 1: { - // Hpolytope - Hpolytope HP(dim, Rcpp::as(Rcpp::as(P).field("A")), - Rcpp::as(Rcpp::as(P).field("b"))); - - InnerBall = HP.ComputeInnerBall(); - if (InnerBall.second < 0.0) throw Rcpp::exception("Unable to compute a feasible point."); - if (!set_starting_point || (!set_mode && gaussian)) { - if (!set_starting_point) StartingPoint = InnerBall.first; - if (!set_mode && gaussian) mode = InnerBall.first; - } - if (HP.is_in(StartingPoint) == 0) { - throw Rcpp::exception("The given point is not in the interior of the polytope!"); - } - if (gaussian) { - StartingPoint = StartingPoint - mode; - HP.shift(mode.getCoefficients()); - } - if (functor_defined) { - sample_from_polytope(HP, type, rng, randPoints, walkL, numpoints, gaussian, a, L, c, - StartingPoint, nburns, set_L, eta, walk, F, f, h, solver); - } - else { - sample_from_polytope(HP, type, rng, randPoints, walkL, numpoints, gaussian, a, L, c, - StartingPoint, nburns, set_L, eta, walk, G, g, hess_g, solver); - } - break; - } - case 2: { - // Vpolytope - Vpolytope VP(dim, Rcpp::as(Rcpp::as(P).field("V")), - VT::Ones(Rcpp::as(Rcpp::as(P).field("V")).rows())); - - InnerBall = VP.ComputeInnerBall(); - if (InnerBall.second < 0.0) throw Rcpp::exception("Unable to compute a feasible point."); - if (!set_starting_point || (!set_mode && gaussian)) { - if (!set_starting_point) StartingPoint = InnerBall.first; - if (!set_mode && gaussian) mode = InnerBall.first; - } - if (VP.is_in(StartingPoint) == 0) - throw Rcpp::exception("The given point is not in the interior of the polytope!"); - if (gaussian) { - StartingPoint = StartingPoint - mode; - VP.shift(mode.getCoefficients()); - } - sample_from_polytope(VP, type, rng, randPoints, walkL, numpoints, gaussian, a, L, c, - StartingPoint, nburns, set_L, eta, walk, F, f, h, solver); - break; - } - case 3: { - // Zonotope - zonotope ZP(dim, Rcpp::as(Rcpp::as(P).field("G")), - VT::Ones(Rcpp::as(Rcpp::as(P).field("G")).rows())); - - InnerBall = ZP.ComputeInnerBall(); - if (InnerBall.second < 0.0) throw Rcpp::exception("Unable to compute a feasible point."); - if (!set_starting_point || (!set_mode && gaussian)) { - if (!set_starting_point) StartingPoint = InnerBall.first; - if (!set_mode && gaussian) mode = InnerBall.first; - } - if (ZP.is_in(StartingPoint) == 0) - throw Rcpp::exception("The given point is not in the interior of the polytope!"); - if (gaussian) { - StartingPoint = StartingPoint - mode; - ZP.shift(mode.getCoefficients()); - } - sample_from_polytope(ZP, type, rng, randPoints, walkL, numpoints, gaussian, a, L, c, - StartingPoint, nburns, set_L, eta, walk, F, f, h, solver); - break; - } - case 4: { - // Intersection of two V-polytopes - Vpolytope VP1(dim, Rcpp::as(Rcpp::as(P).field("V1")), - VT::Ones(Rcpp::as(Rcpp::as(P).field("V1")).rows())); - Vpolytope VP2(dim, Rcpp::as(Rcpp::as(P).field("V2")), - VT::Ones(Rcpp::as(Rcpp::as(P).field("V2")).rows())); - InterVP VPcVP(VP1, VP2); - - if (!VPcVP.is_feasible()) throw Rcpp::exception("Empty set!"); - InnerBall = VPcVP.ComputeInnerBall(); - if (InnerBall.second < 0.0) throw Rcpp::exception("Unable to compute a feasible point."); - if (!set_starting_point) StartingPoint = InnerBall.first; - if (!set_mode && gaussian) mode = InnerBall.first; - if (VPcVP.is_in(StartingPoint) == 0) - throw Rcpp::exception("The given point is not in the interior of the polytope!"); - if (gaussian) { - StartingPoint = StartingPoint - mode; - VPcVP.shift(mode.getCoefficients()); - } - sample_from_polytope(VPcVP, type, rng, randPoints, walkL, numpoints, gaussian, a, L, c, - StartingPoint, nburns, set_L, eta, walk, F, f, h, solver); - break; - } - case 5: { - // Sparse constraint_problem - SpMat Aeq = Rcpp::as(Rcpp::as(P).field("Aeq")); - VT beq= Rcpp::as(Rcpp::as(P).field("beq")); - SpMat Aineq = Rcpp::as(Rcpp::as(P).field("Aineq")); - VT bineq= Rcpp::as(Rcpp::as(P).field("bineq")); - VT lb= Rcpp::as(Rcpp::as(P).field("lb")); - VT ub= Rcpp::as(Rcpp::as(P).field("ub")); - sparse_problem problem(dim, Aeq, beq, Aineq, bineq, lb, ub); - if(walk!=crhmc){throw Rcpp::exception("Sparse problems are supported only by the CRHMC walk.");} - if (functor_defined) { - execute_crhmc, RcppFunctor::GradientFunctor,RcppFunctor::FunctionFunctor, RcppFunctor::HessianFunctor, CRHMCWalk, 8>(problem, rng, randPoints, walkL, numpoints, nburns, F, f, h); - } - else { - execute_crhmc, GaussianFunctor::GradientFunctor,GaussianFunctor::FunctionFunctor, GaussianFunctor::HessianFunctor, CRHMCWalk, 8>(problem, rng, randPoints, walkL, numpoints, nburns, G, g, hess_g); - } - break; - } - } - - if (numpoints % 2 == 1 && (walk == brdhr || walk == bcdhr)) numpoints--; - MT RetMat(dim, numpoints); - unsigned int jj = 0; - - for (typename std::list::iterator rpit = randPoints.begin(); rpit!=randPoints.end(); rpit++, jj++) { - if (gaussian) { - RetMat.col(jj) = (*rpit).getCoefficients() + mode.getCoefficients(); - } else { - RetMat.col(jj) = (*rpit).getCoefficients(); - } - } - - return Rcpp::wrap(RetMat); -} diff --git a/R-proj/src/spectrahedron.cpp b/R-proj/src/spectrahedron.cpp deleted file mode 100644 index e7926597c..000000000 --- a/R-proj/src/spectrahedron.cpp +++ /dev/null @@ -1,117 +0,0 @@ -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2020 Vissarion Fisikopoulos -// Copyright (c) 2020 Apostolos Chalkis - -//Contributed and/or modified by Repouskos Panagiotis, as part of Google Summer of Code 2019 program. - -// Licensed under GNU LGPL.3, see LICENCE file - - -#include -#include -#include -#include "cartesian_geom/cartesian_kernel.h" -#include -#include -#include -#include -#include "convex_bodies/spectrahedra/LMI.h" -#include "convex_bodies/spectrahedra/spectrahedron.h" -#include "SDPAFormatManager.h" - -//' Write a SDPA format file -//' -//' Outputs a spectrahedron (the matrices defining a linear matrix inequality) and a vector (the objective function) -//' to a SDPA format file. -//' -//' @param spectrahedron A spectrahedron in n dimensions; must be an object of class Spectrahedron -//' @param objectiveFunction A numerical vector of length n -//' @param outputFile Name of the output file -//' -//' @examples -//' \dontrun{ -//' A0 = matrix(c(-1,0,0,0,-2,1,0,1,-2), nrow=3, ncol=3, byrow = TRUE) -//' A1 = matrix(c(-1,0,0,0,0,1,0,1,0), nrow=3, ncol=3, byrow = TRUE) -//' A2 = matrix(c(0,0,-1,0,0,0,-1,0,0), nrow=3, ncol=3, byrow = TRUE) -//' lmi = list(A0, A1, A2) -//' S = Spectrahedron$new(lmi); -//' objFunction = c(1,1) -//' writeSdpaFormatFile(S, objFunction, "output.txt") -//' } -//' @export -// [[Rcpp::export]] -void writeSdpaFormatFile(Rcpp::Nullable spectrahedron = R_NilValue, - Rcpp::Nullable objectiveFunction = R_NilValue, - Rcpp::Nullable outputFile = R_NilValue) { - - typedef double NT; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - typedef Cartesian Kernel; - typedef typename Kernel::Point Point; - typedef boost::mt19937 RNGType; - typedef LMI LMI; - typedef Spectrahedron Spectrahedron; - - std::vector matrices = Rcpp::as > (Rcpp::as (spectrahedron).field("matrices")); - LMI lmi(matrices); - Spectrahedron _spectrahedron(lmi); - Point c(Rcpp::as (objectiveFunction)); - - std::filebuf fb; - fb.open(Rcpp::as (outputFile), std::ios::out); - std::ostream os(&fb); - - SdpaFormatManager sdpaFormatManager; - sdpaFormatManager.writeSDPAFormatFile(os, _spectrahedron, c); - - return; -} - - - -//' Read a SDPA format file -//' -//' @param inputFile Name of the input file -//' -//' @return A list with two named items: an item "matrices" which is a list of the matrices and an vector "objFunction" -//' -//' @examples -//' path = system.file('extdata', package = 'volesti') -//' l = loadSdpaFormatFile(paste0(path,'/sdpa_n2m3.txt')) -//' @export -// [[Rcpp::export]] -Rcpp::List loadSdpaFormatFile(Rcpp::Nullable inputFile = R_NilValue) { - - typedef double NT; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - typedef Cartesian Kernel; - typedef typename Kernel::Point Point; - typedef boost::mt19937 RNGType; - typedef LMI LMI; - typedef Spectrahedron Spectrahedron; - - Spectrahedron _spectrahedron; - Point c; - - // open stream - std::ifstream os; - os.open(Rcpp::as (inputFile),std::ifstream::in); - - // read file - SdpaFormatManager sdpaFormatManager; - sdpaFormatManager.loadSDPAFormatFile(os, _spectrahedron, c); - - std::vector matrices = _spectrahedron.getLMI().getMatrices(); - - // return spectrahedron and objective function - Rcpp::List _matrices; - - for (auto matrix : matrices) - _matrices.push_back(Rcpp::wrap(matrix)); - - Rcpp::List retList = Rcpp::List::create(Rcpp::Named("matrices") = _matrices , Rcpp::_["objFunction"] = Rcpp::wrap(c.getCoefficients())); - return Rcpp::wrap(retList); -} diff --git a/R-proj/src/spectrahedron_module.cpp b/R-proj/src/spectrahedron_module.cpp deleted file mode 100644 index 8dedd2dc1..000000000 --- a/R-proj/src/spectrahedron_module.cpp +++ /dev/null @@ -1,48 +0,0 @@ -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2020 Vissarion Fisikopoulos -// Copyright (c) 2020 Apostolos Chalkis - -//Contributed and/or modified by Repouskos Panagiotis, as part of Google Summer of Code 2019 program. - -// Licensed under GNU LGPL.3, see LICENCE file - - -#include - -class Spectrahedron { -public: - /// A list with the matrices A0, ..., An - Rcpp::List matrices; - - Spectrahedron() {} - - Spectrahedron(Rcpp::List _matrices) : matrices(_matrices) {} -}; - -RCPP_MODULE(spectrahedron){ - using namespace Rcpp ; - - //' An exposed class to represent a spectrahedron - //' - //' @description A spectrahedron is a convex body defined by a linear matrix inequality of the form \eqn{A_0 + x_1 A_1 + ... + x_n A_n \preceq 0}. - //' The matrices \eqn{A_i} are symmetric \eqn{m \times m} real matrices and \eqn{\preceq 0} denoted negative semidefiniteness. - //' - //' @field matrices The matrices \eqn{A_0, A_1, ..., A_n} - //' - //' @example - //' A0 = matrix(c(-1,0,0,0,-2,1,0,1,-2), nrow=3, ncol=3, byrow = TRUE) - //' A1 = matrix(c(-1,0,0,0,0,1,0,1,0), nrow=3, ncol=3, byrow = TRUE) - //' A2 = matrix(c(0,0,-1,0,0,0,-1,0,0), nrow=3, ncol=3, byrow = TRUE) - //' lmi = list(M0, M1,M2) - //' S = Spectrahedron$new(lmi); - //' - //' @export - class_("Spectrahedron") - // expose the default constructor - .constructor() - .constructor() - .field( "matrices", &Spectrahedron::matrices); -} - -extern SEXP _rcpp_module_boot_spectrahedron(void); diff --git a/R-proj/src/volume.cpp b/R-proj/src/volume.cpp deleted file mode 100644 index 0a47210aa..000000000 --- a/R-proj/src/volume.cpp +++ /dev/null @@ -1,407 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2020 Vissarion Fisikopoulos -// Copyright (c) 2018-2020 Apostolos Chalkis - -//Contributed and/or modified by Apostolos Chalkis, as part of Google Summer of Code 2018 and 2019 program. - - -#include -#include -#include -#include -#include -#include -#include "random_walks/random_walks.hpp" -#include "volume/volume_cooling_gaussians.hpp" -#include "volume/volume_sequence_of_balls.hpp" -#include "volume/volume_cooling_gaussians.hpp" -#include "volume/volume_cooling_balls.hpp" -#include "volume/volume_cooling_hpoly.hpp" -#include "preprocess/max_inscribed_ellipsoid_rounding.hpp" -#include "preprocess/svd_rounding.hpp" - -enum random_walks {ball_walk, rdhr, cdhr, billiard, accelarated_billiard}; -enum volume_algorithms {CB, CG, SOB}; -enum rounding_type {none, min_ellipsoid, max_ellipsoid, isotropy}; - -template -std::pair generic_volume(Polytope& P, RNGType &rng, unsigned int walk_length, NT e, - volume_algorithms const& algo, unsigned int win_len, - rounding_type const& rounding, random_walks const& walk) -{ - typedef typename Polytope::MT MT; - typedef typename Polytope::VT VT; - typedef typename Polytope::PointType Point; - - typedef HPolytope Hpolytope; - - NT round_val = 1.0; - unsigned int n = P.dimension(); - std::pair InnerBall; - - if (rounding != none){ - InnerBall = P.ComputeInnerBall(); - if (InnerBall.second < 0.0) throw Rcpp::exception("Unable to compute a feasible point."); - } - - switch (rounding) - { - case min_ellipsoid: - switch (walk) - { - case cdhr: - round_val = std::get<2>(min_sampling_covering_ellipsoid_rounding(P, InnerBall, 10 + 10 * n, rng)); - break; - case accelarated_billiard: - round_val = std::get<2>(min_sampling_covering_ellipsoid_rounding(P, InnerBall, 2, rng)); - break; - default: - round_val = std::get<2>(min_sampling_covering_ellipsoid_rounding(P, InnerBall, 2, rng)); - break; - } - break; - case isotropy: - switch (walk) - { - case cdhr: - round_val = std::get<2>(svd_rounding(P, InnerBall, 10 + 10 * n, rng)); - break; - case accelarated_billiard: - round_val = std::get<2>(svd_rounding(P, InnerBall, 2, rng)); - break; - default: - round_val = std::get<2>(svd_rounding(P, InnerBall, 2, rng)); - break; - } - break; - case max_ellipsoid: - round_val = std::get<2>(max_inscribed_ellipsoid_rounding(P, InnerBall.first)); - break; - default: - break; - } - - NT vol; - std::pair pair_vol; - switch (algo) - { - case CG: - switch (walk) - { - case cdhr: - vol = volume_cooling_gaussians(P, rng, e, walk_length); - pair_vol = std::pair (std::log(vol), vol); - break; - case rdhr: - vol = volume_cooling_gaussians(P, rng, e, walk_length); - pair_vol = std::pair (std::log(vol), vol); - break; - case ball_walk: - vol = volume_cooling_gaussians(P, rng, e, walk_length); - pair_vol = std::pair (std::log(vol), vol); - break; - default: - throw Rcpp::exception("This random walk can not be used by CG algorithm!"); - break; - } - break; - case CB: - switch (walk) - { - case cdhr: - pair_vol = volume_cooling_balls(P, rng, e, walk_length, win_len); - break; - case rdhr: - pair_vol = volume_cooling_balls(P, rng, e, walk_length, win_len); - break; - case ball_walk: - pair_vol = volume_cooling_balls(P, rng, e, walk_length, win_len); - break; - case billiard: - pair_vol = volume_cooling_balls(P, rng, e, walk_length, win_len); - break; - case accelarated_billiard: - pair_vol = volume_cooling_balls(P, rng, e, walk_length, win_len); - break; - default: - throw Rcpp::exception("This random walk can not be used by CB algorithm!"); - break; - } - break; - case SOB: - switch (walk) - { - case cdhr: - vol = volume_sequence_of_balls(P, rng, e, walk_length); - pair_vol = std::pair (std::log(vol), vol); - break; - case rdhr: - vol = volume_sequence_of_balls(P, rng, e, walk_length); - pair_vol = std::pair (std::log(vol), vol); - break; - case ball_walk: - vol = volume_sequence_of_balls(P, rng, e, walk_length); - pair_vol = std::pair (std::log(vol), vol); - break; - case billiard: - vol = volume_sequence_of_balls(P, rng, e, walk_length); - break; - case accelarated_billiard: - vol = volume_sequence_of_balls(P, rng, e, walk_length); - pair_vol = std::pair (std::log(vol), vol); - break; - default: - throw Rcpp::exception("This random walk can not be used by CB algorithm!"); - break; - } - break; - default: - throw Rcpp::exception("Unknown algorithm!"); - break; - } - if (pair_vol.second < 0.0) throw Rcpp::exception("volesti failed to terminate."); - pair_vol.first += std::log(round_val); - pair_vol.second *= round_val; - return pair_vol; -} - -//' The main function for volume approximation of a convex Polytope (H-polytope, V-polytope, zonotope or intersection of two V-polytopes). It returns a list with two elements: (a) the logarithm of the estimated volume and (b) the estimated volume -//' -//' For the volume approximation can be used three algorithms. Either CoolingBodies (CB) or SequenceOfBalls (SOB) or CoolingGaussian (CG). An H-polytope with \eqn{m} facets is described by a \eqn{m\times d} matrix \eqn{A} and a \eqn{m}-dimensional vector \eqn{b}, s.t.: \eqn{P=\{x\ |\ Ax\leq b\} }. A V-polytope is defined as the convex hull of \eqn{m} \eqn{d}-dimensional points which correspond to the vertices of P. A zonotope is desrcibed by the Minkowski sum of \eqn{m} \eqn{d}-dimensional segments. -//' -//' @param P A convex polytope. It is an object from class a) Hpolytope or b) Vpolytope or c) Zonotope or d) VpolytopeIntersection. -//' @param settings Optional. A list that declares which algorithm, random walk and values of parameters to use, as follows: -//' \itemize{ -//' \item{\code{algorithm} }{ A string to set the algorithm to use: a) \code{'CB'} for CB algorithm, b) \code{'SoB'} for SOB algorithm or b) \code{'CG'} for CG algorithm. The defalut algorithm is \code{'CB'}.} -//' \item{\code{error} }{ A numeric value to set the upper bound for the approximation error. The default value is \eqn{1} for SOB algorithm and \eqn{0.1} otherwise.} -//' \item{\code{random_walk} }{ A string that declares the random walk method: a) \code{'CDHR'} for Coordinate Directions Hit-and-Run, b) \code{'RDHR'} for Random Directions Hit-and-Run, c) \code{'BaW'} for Ball Walk, or \code{'BiW'} for Billiard walk. For CB algorithm the default walk is \code{'BiW'}. For CG and SOB algorithms the default walk is \code{'CDHR'} for H-polytopes and \code{'RDHR'} for the other representations.} -//' \item{\code{walk_length} }{ An integer to set the number of the steps for the random walk. The default value is \eqn{\lfloor 10 + d/10\rfloor} for \code{'SOB'} and \eqn{1} otherwise.} -//' \item{\code{win_len} }{ The length of the sliding window for CB or CG algorithm. The default value is \eqn{250} for CB with BiW and \eqn{400+3d^2} for CB and any other random walk and \eqn{500+4d^2} for CG.} -//' \item{\code{hpoly} }{ A boolean parameter to use H-polytopes in MMC of CB algorithm when the input polytope is a zonotope. The default value is \code{TRUE} when the order of the zonotope is \eqn{<5}, otherwise it is \code{FALSE}.} -//' } -//' @param rounding Optional. A string parameter to request a rounding method to be applied in the input polytope before volume computation: a) \code{'min_ellipsoid'}, b) \code{'svd'}, c) \code{'max_ellipsoid'} and d) \code{'none'} for no rounding. -//' @param seed Optional. A fixed seed for the number generator. -//' -//' @references \cite{I.Z.Emiris and V. Fisikopoulos, -//' \dQuote{Practical polytope volume approximation,} \emph{ACM Trans. Math. Soft.,} 2018.}, -//' @references \cite{A. Chalkis and I.Z.Emiris and V. Fisikopoulos, -//' \dQuote{Practical Volume Estimation by a New Annealing Schedule for Cooling Convex Bodies,} \emph{CoRR, abs/1905.05494,} 2019.}, -//' @references \cite{B. Cousins and S. Vempala, \dQuote{A practical volume algorithm,} \emph{Springer-Verlag Berlin Heidelberg and The Mathematical Programming Society,} 2015.} -//' -//' -//' @return The approximation of the volume of a convex polytope. -//' @examples -//' -//' # calling SOB algorithm for a H-polytope (5d unit simplex) -//' HP = gen_cube(5,'H') -//' pair_vol = volume(HP) -//' -//' # calling CG algorithm for a V-polytope (3d simplex) -//' VP = gen_simplex(3,'V') -//' pair_vol = volume(VP, settings = list("algorithm" = "CG")) -//' -//' # calling CG algorithm for a 2-dimensional zonotope defined as the Minkowski sum of 4 segments -//' Z = gen_rand_zonotope(2, 4) -//' pair_vol = volume(Z, settings = list("random_walk" = "RDHR", "walk_length" = 2)) -//' -//' @export -// [[Rcpp::export]] -Rcpp::List volume (Rcpp::Reference P, - Rcpp::Nullable settings = R_NilValue, - Rcpp::Nullable rounding = R_NilValue, - Rcpp::Nullable seed = R_NilValue) { - - typedef double NT; - typedef Cartesian Kernel; - typedef typename Kernel::Point Point; - typedef BoostRandomNumberGenerator RNGType; - typedef HPolytope Hpolytope; - typedef VPolytope Vpolytope; - typedef Zonotope zonotope; - typedef IntersectionOfVpoly InterVP; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - unsigned int n = P.field("dimension"), walkL, type = P.field("type"); - - RNGType rng(n); - if (seed.isNotNull()) { - unsigned seed_rcpp = Rcpp::as(seed); - rng.set_seed(seed_rcpp); - } - - bool round = false, hpoly = false; - unsigned int win_len = 300; - - random_walks walk; - volume_algorithms algo; - rounding_type rounding_method; - - if (!rounding.isNotNull()) { - rounding_method = (type == 2) ? min_ellipsoid : none; - } else if (Rcpp::as(rounding).compare(std::string("min_ellipsoid")) == 0) { - rounding_method = min_ellipsoid; - } else if (Rcpp::as(rounding).compare(std::string("max_ellipsoid")) == 0) { - if (type != 1) throw Rcpp::exception("This rounding method can be used only for H-polytopes!"); - rounding_method = max_ellipsoid; - } else if (Rcpp::as(rounding).compare(std::string("isotropy")) == 0) { - rounding_method = isotropy; - } else if (Rcpp::as(rounding).compare(std::string("none")) == 0) { - rounding_method = none; - } - - NT e; - - if (!Rcpp::as(settings).containsElementNamed("algorithm")) { - algo = CB; - walkL = (!Rcpp::as(settings).containsElementNamed("walk_length")) ? 1 : Rcpp::as( - Rcpp::as(settings)["walk_length"]); - e = (!Rcpp::as(settings).containsElementNamed("error")) ? 0.1 : Rcpp::as( - Rcpp::as(settings)["error"]); - } else if (Rcpp::as(Rcpp::as(settings)["algorithm"]).compare(std::string("SOB")) == 0) { - algo = SOB; - walkL = (!Rcpp::as(settings).containsElementNamed("walk_length")) ? 10 + n / 10 : Rcpp::as( - Rcpp::as(settings)["walk_length"]); - e = (!Rcpp::as(settings).containsElementNamed("error")) ? 1.0 : Rcpp::as( - Rcpp::as(settings)["error"]); - } else if (Rcpp::as(Rcpp::as(settings)["algorithm"]).compare(std::string("CG")) == 0) { - algo = CG; - walkL = (!Rcpp::as(settings).containsElementNamed("walk_length")) ? 1 : Rcpp::as( - Rcpp::as(settings)["walk_length"]); - e = (!Rcpp::as(settings).containsElementNamed("error")) ? 0.1 : Rcpp::as( - Rcpp::as(settings)["error"]); - } else if (Rcpp::as(Rcpp::as(settings)["algorithm"]).compare(std::string("CB")) == 0) { - algo = CB; - walkL = (!Rcpp::as(settings).containsElementNamed("walk_length")) ? 1 : Rcpp::as( - Rcpp::as(settings)["walk_length"]); - e = (!Rcpp::as(settings).containsElementNamed("error")) ? 0.1 : Rcpp::as( - Rcpp::as(settings)["error"]); - } else { - throw Rcpp::exception("Unknown method!"); - } - - if (!Rcpp::as(settings).containsElementNamed("random_walk")) { - if (algo == CB) { - walk = (type == 1) ? accelarated_billiard : billiard; - } else { - win_len = 4 * n * n + 500; - if (type == 1) { - walk = cdhr; - } else { - walk = rdhr; - } - } - }else if (Rcpp::as(Rcpp::as(settings)["random_walk"]).compare(std::string("CDHR")) == 0) { - walk = cdhr; - if (algo == CB) win_len = 3*n*n+400; - } else if (Rcpp::as(Rcpp::as(settings)["random_walk"]).compare(std::string("RDHR")) == 0) { - walk = rdhr; - if (algo == CB) win_len = 3*n*n+400; - } else if (Rcpp::as(Rcpp::as(settings)["random_walk"]).compare(std::string("BaW")) == 0) { - walk = ball_walk; - if (algo == CB) win_len = 3*n*n+400; - } else if (Rcpp::as(Rcpp::as(settings)["random_walk"]).compare(std::string("BiW")) == 0) { - if (algo == CG) { - if (type !=1){ - Rcpp::Rcout << "Billiard walk is not supported for CG algorithm. RDHR is used."<(settings).containsElementNamed("win_len")) { - win_len = Rcpp::as(Rcpp::as(settings)["win_len"]); - if (algo == SOB) Rf_warning("input 'win_len' can be used only for CG or CB algorithms."); - } - - std::pair pair_vol; - NT vol; - - switch(type) { - case 1: { - // Hpolytope - Hpolytope HP(n, Rcpp::as(P.field("A")), Rcpp::as(P.field("b"))); - pair_vol = generic_volume(HP, rng, walkL, e, algo, win_len, rounding_method, walk); - break; - } - case 2: { - // Vpolytope - Vpolytope VP(n, Rcpp::as(P.field("V")), VT::Ones(Rcpp::as(P.field("V")).rows())); - pair_vol = generic_volume(VP, rng, walkL, e, algo, win_len, rounding_method, walk); - break; - } - case 3: { - // Zonotope - zonotope ZP(n, Rcpp::as(P.field("G")), VT::Ones(Rcpp::as(P.field("G")).rows())); - if (Rcpp::as(settings).containsElementNamed("hpoly")) { - hpoly = Rcpp::as(Rcpp::as(settings)["hpoly"]); - if (hpoly && (algo == CG || algo == SOB)) - Rf_warning("flag 'hpoly' can be used to only in MMC of CB algorithm for zonotopes."); - } else if (ZP.num_of_generators() / ZP.dimension() < 5) { - hpoly = true; - } else { - hpoly = false; - } - if (hpoly && algo == CB) { - switch (walk) - { - case cdhr: - vol = volume_cooling_hpoly(ZP, rng, e, walkL, win_len); - pair_vol = std::pair (std::log(vol), vol); - break; - case rdhr: - vol = volume_cooling_hpoly(ZP, rng, e, walkL, win_len); - pair_vol = std::pair (std::log(vol), vol); - break; - case ball_walk: - vol = volume_cooling_hpoly(ZP, rng, e, walkL, win_len); - pair_vol = std::pair (std::log(vol), vol); - break; - case billiard: - vol = volume_cooling_hpoly(ZP, rng, e, walkL, win_len); - pair_vol = std::pair (std::log(vol), vol); - break; - case accelarated_billiard: - vol = volume_cooling_hpoly(ZP, rng, e, walkL, win_len); - pair_vol = std::pair (std::log(vol), vol); - break; - default: - throw Rcpp::exception("This random walk can not be used by CB algorithm!"); - break; - } - } - pair_vol = generic_volume(ZP, rng, walkL, e, algo, win_len, rounding_method, walk); - break; - } - case 4: { - // Intersection of two V-polytopes - Vpolytope VP1(n, Rcpp::as(P.field("V1")), VT::Ones(Rcpp::as(P.field("V1")).rows())); - Vpolytope VP2(n, Rcpp::as(P.field("V2")), VT::Ones(Rcpp::as(P.field("V2")).rows())); - InterVP VPcVP; - if (!seed.isNotNull()) { - InterVP VPcVP(VP1, VP2); - } else { - unsigned seed3 = Rcpp::as(seed); - InterVP VPcVP(VP1, VP2, seed3); - } - if (!VPcVP.is_feasible()) throw Rcpp::exception("Empty set!"); - pair_vol = generic_volume(VPcVP, rng, walkL, e, algo, win_len, rounding_method, walk); - break; - } - } - - return Rcpp::List::create(Rcpp::Named("log_volume") = pair_vol.first, Rcpp::Named("volume") = pair_vol.second); -} diff --git a/R-proj/src/zonotope_approximation.cpp b/R-proj/src/zonotope_approximation.cpp deleted file mode 100644 index 237ec96e1..000000000 --- a/R-proj/src/zonotope_approximation.cpp +++ /dev/null @@ -1,102 +0,0 @@ -// [[Rcpp::depends(BH)]] - -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2018 Vissarion Fisikopoulos -// Copyright (c) 2018 Apostolos Chalkis - - -#include -#include -#include -#include -#include -#include -#include "random_walks/random_walks.hpp" -#include "volume/volume_sequence_of_balls.hpp" -#include "volume/volume_cooling_gaussians.hpp" -#include "volume/volume_cooling_balls.hpp" -#include "volume/volume_cooling_hpoly.hpp" - -//' An internal Rccp function for the over-approximation of a zonotope -//' -//' @param Z A zonotope. -//' @param fit_ratio Optional. A boolean parameter to request the computation of the ratio of fitness. -//' @param settings Optional. A list that declares the values of the parameters of CB algorithm. -//' @param seed Optional. A fixed seed for the number generator. -//' -//' @keywords internal -//' -//' @return A List that contains a numerical matrix that describes the PCA approximation as a H-polytope and the ratio of fitness. -// [[Rcpp::export]] -Rcpp::List zono_approx (Rcpp::Reference Z, - Rcpp::Nullable fit_ratio = R_NilValue, - Rcpp::Nullable settings = R_NilValue, - Rcpp::Nullable seed = R_NilValue) { - - typedef double NT; - typedef Cartesian Kernel; - typedef BoostRandomNumberGenerator RNGType; - typedef typename Kernel::Point Point; - typedef HPolytope Hpolytope; - typedef Zonotope zonotope; - typedef Eigen::Matrix VT; - typedef Eigen::Matrix MT; - int n = Rcpp::as(Z.field("dimension")), k = Rcpp::as(Z.field("G")).rows(), win_len = 200, walkL = 1; - - RNGType rng(n); - if (seed.isNotNull()) { - unsigned seed_rcpp = Rcpp::as(seed); - rng.set_seed(seed_rcpp); - } - - NT e = 0.1, ratio = std::numeric_limits::signaling_NaN(); - bool hpoly = false; - - MT X(n, 2 * k); - X << Rcpp::as(Z.field("G")).transpose(), -Rcpp::as(Z.field("G")).transpose(); - Eigen::JacobiSVD svd(X * X.transpose(), Eigen::ComputeFullU | Eigen::ComputeFullV); - MT G(k, 2 * n); - G << Rcpp::as(Z.field("G")) * svd.matrixU(), Rcpp::as(Z.field("G")) * svd.matrixU(); - VT Gred_ii = G.transpose().cwiseAbs().rowwise().sum(); - MT A(n, 2 * n); - A << -MT::Identity(n, n), MT::Identity(n, n); - MT Mat(2 * n, n + 1); - Mat << Gred_ii, A.transpose() * svd.matrixU().transpose(); - - //Hpolytope HP(n, A.transpose() * svd.matrixU().transpose(), Gred_ii); - - if (fit_ratio.isNotNull() && Rcpp::as(fit_ratio)) { - NT vol_red = std::abs(svd.matrixU().determinant()); - for (int i = 0; i < n; ++i) { - vol_red *= 2.0 * Gred_ii(i); - } - - walkL = (!Rcpp::as(settings).containsElementNamed("walk_length")) ? 1 : Rcpp::as( - Rcpp::as(settings)["walk_length"]); - e = (!Rcpp::as(settings).containsElementNamed("error")) ? 0.1 : Rcpp::as( - Rcpp::as(settings)["error"]); - win_len = (!Rcpp::as(settings).containsElementNamed("win_len")) ? 200 : Rcpp::as( - Rcpp::as(settings)["win_len"]); - - zonotope ZP(n, Rcpp::as(Z.field("G")), VT::Ones(Rcpp::as(Z.field("G")).rows())); - - if (Rcpp::as(settings).containsElementNamed("hpoly")) { - hpoly = Rcpp::as(Rcpp::as(settings)["hpoly"]); - } else if (ZP.num_of_generators() / ZP.dimension() < 5 ) { - hpoly = true; - } else { - hpoly = false; - } - - NT vol; - if (!hpoly) { - vol = volume_cooling_balls(ZP, rng, e, walkL, win_len).second; - } else { - vol = volume_cooling_hpoly(ZP, rng, e, walkL, win_len); - } - ratio = std::pow(vol_red / vol, 1.0 / NT(n)); - } - - return Rcpp::List::create(Rcpp::Named("Mat") = Rcpp::wrap(Mat), Rcpp::Named("fit_ratio") = ratio); -} diff --git a/R-proj/tests/testthat.R b/R-proj/tests/testthat.R deleted file mode 100644 index 051a041f8..000000000 --- a/R-proj/tests/testthat.R +++ /dev/null @@ -1,3 +0,0 @@ -library(testthat) - -test_check("volesti") diff --git a/R-proj/tests/testthat/test_Hvol.R b/R-proj/tests/testthat/test_Hvol.R deleted file mode 100644 index 061a33acf..000000000 --- a/R-proj/tests/testthat/test_Hvol.R +++ /dev/null @@ -1,66 +0,0 @@ -context("H-polytopes' volume test") - -library(volesti) - - -Hruntest <- function(P, name_string, exactvol, tol, num_of_exps, alg, seed){ - - vol = 0 - for (j in 1:num_of_exps) { - if (alg == "CB") { - vol = vol + volume(P, seed = seed)$volume - } else if (alg == "SOB") { - vol = vol + volume(P, settings = list("algorithm" = "SOB"), seed = seed)$volume - } else { - vol = vol + volume(P, settings = list("algorithm" = "CG"), seed = seed)$volume - } - } - vol = vol / num_of_exps - error = abs(vol - exactvol) / exactvol - if (error >= tol){ - res = 0 - } else { - res = 1 - } - return(res) -} - -cran_only = TRUE - -for (i in 1:2) { - - if (i==1) { - algo = 'CG' - num_of_exps = 10 - } else { - algo = 'CB' - num_of_exps = 10 - } - - - test_that("Volume H-cube10", { - P = gen_cube(10, 'H') - res = Hruntest(P, 'H-cube10', 1024, 0.2, num_of_exps, algo, 5) - expect_equal(res, 1) - }) - - test_that("Volume H-cross5", { - P = gen_cross(5, 'H') - res = Hruntest(P, 'H-cross10', 0.2666667, 0.2, num_of_exps, algo, 5) - expect_equal(res, 1) - }) - - - test_that("Volume H-prod_simplex_5_5", { - P = gen_prod_simplex(5) - res = Hruntest(P, 'H-prod_simplex_5_5', (1/prod(1:5))^2, 0.2, num_of_exps, algo, 5) - expect_equal(res, 1) - }) - - test_that("Volume H-cube10", { - P = gen_cube(10, 'H') - res = Hruntest(P, 'H-cube10', 1024, 0.2, 5, "SOB", 5) - expect_equal(res, 1) - }) - -} diff --git a/R-proj/tests/testthat/test_InnerBall.R b/R-proj/tests/testthat/test_InnerBall.R deleted file mode 100644 index cb19e1c00..000000000 --- a/R-proj/tests/testthat/test_InnerBall.R +++ /dev/null @@ -1,135 +0,0 @@ -context("Chebychev ball test") - -library(volesti) - -runCheTest <- function(P, name_string, radius, tol) { - - vec_ball = inner_ball(P, lpsolve = TRUE) - rad = vec_ball[length(vec_ball)] - - error = abs(radius - rad) / radius - if (error >= tol){ - res = 0 - } else { - res = 1 - } - return(res) -} - -tol = 0.00001 - -test_that("Chebychev test", { - P = gen_cube(10, 'H') - res = runCheTest(P, 'H-cube10', 1.0, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_cube(20, 'H') - res = runCheTest(P, 'H-cube20', 1.0, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_cube(30, 'H') - res = runCheTest(P, 'H-cube30', 1.0, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_cross(10, 'H') - res = runCheTest(P, 'H-cross10', 0.316228, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_birkhoff(3) - res = runCheTest(P, 'H-birk3', 0.207107, tol) - expect_equal(res, 1) -}) - - -test_that("Chebychev test", { - P = gen_birkhoff(4) - res = runCheTest(P, 'H-birk4', 0.122008, tol) - expect_equal(res, 1) -}) - - -test_that("Chebychev test", { - P = gen_birkhoff(5) - res = runCheTest(P, 'H-birk5', 0.0833333, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_birkhoff(6) - res = runCheTest(P, 'H-birk6', 0.0618034, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_prod_simplex(5) - res = runCheTest(P, 'H-prod_simplex_5_5', 0.138197, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_prod_simplex(10) - res = runCheTest(P, 'H-prod_simplex_10_10', 0.0759747, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_prod_simplex(15) - res = runCheTest(P, 'H-prod_simplex_15_15', 0.0529858, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_prod_simplex(20) - res = runCheTest(P, 'H-prod_simplex_20_20', 0.0408628, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_simplex(10, 'H') - res = runCheTest(P, 'H-simplex10', 0.0759747, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_simplex(20, 'H') - res = runCheTest(P, 'H-simplex20', 0.0408628, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_simplex(30, 'H') - res = runCheTest(P, 'H-simplex30', 0.0281871, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_simplex(40, 'H') - res = runCheTest(P, 'H-simplex40', 0.0215868, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_simplex(50, 'H') - res = runCheTest(P, 'H-simplex50', 0.017522, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_skinny_cube(10) - res = runCheTest(P, 'H-skinny_cube10', 1.0, tol) - expect_equal(res, 1) -}) - -test_that("Chebychev test", { - P = gen_skinny_cube(20) - res = runCheTest(P, 'H-skinny_cube20', 1.0, tol) - expect_equal(res, 1) -}) diff --git a/R-proj/tests/testthat/test_Vvol.R b/R-proj/tests/testthat/test_Vvol.R deleted file mode 100644 index 9e18e52b2..000000000 --- a/R-proj/tests/testthat/test_Vvol.R +++ /dev/null @@ -1,44 +0,0 @@ -context("V-polytopes' volume test") - -library(volesti) - -Vruntest <- function(P, name_string, exactvol, tol, num_of_exps, algorithm,seed){ - - vol = 0 - for (j in 1:num_of_exps) { - if (algorithm == "CB") { - vol = vol + volume(P, rounding = "none", seed = seed)$volume - } else { - vol = vol + volume(P, settings = list("algorithm" = "CG", "error" = 0.1), rounding = "none", seed = seed)$volume - } - } - vol = vol / num_of_exps - error = abs(vol - exactvol) / exactvol - if (error >= tol){ - res = 0 - } else { - res = 1 - } - return(res) -} - -cran_only = TRUE -num_of_exps = 2 - -for (i in 1:2) { - seed = 5 - if (i==1) { - algo = 'CG' - tol = 0.2 - } else { - algo = 'CB' - tol = 0.2 - } - - test_that("Volume V-simplex3", { - P = gen_simplex(3, 'V') - res = Vruntest(P, 'V-simplex3', 1/prod(1:3), tol, num_of_exps, algo, seed) - expect_equal(res, 1) - }) - -} diff --git a/R-proj/tests/testthat/test_Zvol.R b/R-proj/tests/testthat/test_Zvol.R deleted file mode 100644 index f382b003c..000000000 --- a/R-proj/tests/testthat/test_Zvol.R +++ /dev/null @@ -1,45 +0,0 @@ -context("Zonotopes' volume test") - -library(volesti) - -Zruntest <- function(P, name_string, tol, num_of_exps, algo, seed){ - - exactvol = exact_vol(P) - vol = 0 - for (j in 1:num_of_exps) { - if (algo == "CB") { - vol = vol + volume(P, settings = list("hpoly" = FALSE), rounding = "none", seed = seed)$volume - } else { - vol = vol + volume(P, settings = list("algorithm" = "CG", "error" = 0.1), rounding = "none", seed = seed)$volume - } - } - vol = vol / num_of_exps - error = abs(vol - exactvol) / exactvol - if (error >= tol){ - res = 0 - } else { - res = 1 - } - return(res) -} - -cran_only = TRUE -num_of_exps = 2 - -for (i in 1:2) { - if (i==1) { - algo = 'CG' - tol = 0.2 - } else { - algo = 'CB' - tol = 0.2 - } - - test_that("Volume Zonotope_2_4", { - #skip_if(Sys.info()[["machine"]] %in% c("x86_32")) - Z = gen_rand_zonotope(2, 4, seed = 127) - res = Zruntest(Z, 'Zonotope_2_4', tol, num_of_exps, algo, 5) - expect_equal(res, 1) - }) - -} diff --git a/R-proj/tests/testthat/test_copulas.R b/R-proj/tests/testthat/test_copulas.R deleted file mode 100644 index e0ee51375..000000000 --- a/R-proj/tests/testthat/test_copulas.R +++ /dev/null @@ -1,23 +0,0 @@ -context("Copulas' test") - -library(volesti) - -test_that("10-dimensional 2-hyp_fam copula", { - h1 = h1 = runif(n = 10, min = 1, max = 1000) - h1 = h1 / 1000 - h2 = h2 = runif(n = 10, min = 1, max = 1000) - h2 = h1 / 1000 - cop = copula(r1 = h1 , r2 = h2 , m = 10, n = 100000) - res = sum(cop) - expect_equal(res, 1) -}) - -test_that("20-dimensional 1_hyp_1_ell fam copula", { - h = h = runif(n = 20, min = 1, max = 1000) - h = h / 1000 - E = replicate(20, rnorm(30)) - E = cov(E) - cop = copula(r1 = h , sigma = E , m = 100, n = 100000) - res = sum(cop) - expect_equal(res, 1) -}) \ No newline at end of file diff --git a/R-proj/tests/testthat/test_rounding.R b/R-proj/tests/testthat/test_rounding.R deleted file mode 100644 index 5d308e367..000000000 --- a/R-proj/tests/testthat/test_rounding.R +++ /dev/null @@ -1,49 +0,0 @@ -context("Rounding test") - -library(volesti) - -testRound <- function(P, exactvol, tol, name_string, num_of_exps, algo, rotation,seed){ - - if (rotation) { - P = rand_rotate(P) - listHpoly = round_polytope(P, seed = seed) - } else { - listHpoly = round_polytope(P, seed = seed) - } - vol = 0 - for (j in 1:num_of_exps) { - if (algo == "CB") { - vol = vol + listHpoly$round_value * volume(listHpoly$P, seed = seed)$volume - } else { - vol = vol + listHpoly$round_value * volume(listHpoly$P, settings=list("algorithm"="CG", "error"=0.1), seed = seed)$volume - } - } - vol = vol / num_of_exps - error = abs(vol - exactvol) / exactvol - if (error >= tol){ - res = 0 - } else { - res = 1 - } - return(res) - - -} - -cran_only = TRUE - -for (i in 1:2) { - - num_of_exps = 10 - - - - test_that("Rounding H-skinny_cube10", { - seed=5 - P = gen_skinny_cube(10) - res = testRound(P, 102400, 0.3, 'H-skinny_cube10', num_of_exps, 'CB', FALSE,seed) - expect_equal(res, 1) - }) - - -} diff --git a/R-proj/tests/testthat/test_sampling.R b/R-proj/tests/testthat/test_sampling.R deleted file mode 100644 index d051dc70e..000000000 --- a/R-proj/tests/testthat/test_sampling.R +++ /dev/null @@ -1,91 +0,0 @@ -context("Sampling test") - -library(volesti) - -runsample <- function(P, name_string, dist){ - if (dist == "uniform") { - p = sample_points(P, n = 100) - } else { - p = sample_points(P, n = 100, distribution = list("density" = "gaussian")) - } - if (length(p[is.nan(p)])>0 | length(p[is.infinite(p)])>0) { - res = 0 - } else { - res = 1 - } - return(res) - -} - -logconcave_sample<- function(P,distribution, n_samples ,n_burns){ - if (distribution == "uniform"){ - f <- function(x) (0) - grad_f <- function(x) (0) - L=1 - m=1 - pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "negative_logprob" = f, "negative_logprob_gradient" = grad_f, "L_" = L, "m" = m)) - return(max(psrf_univariate(pts, "interval"))) - } - else if(distribution == "gaussian"){ - pts <- sample_points(P, n = n_samples, random_walk = list("walk" = "CRHMC", "nburns" = n_burns, "walk_length" = 1, "solver" = "implicit_midpoint"), distribution = list("density" = "logconcave", "variance"=8)) - return(max(psrf_univariate(pts, "interval"))) - } -} - -for (i in 1:2) { - - if (i==1) { - distribution = 'gaussian' - } else { - distribution = 'uniform' - } - - test_that("Sampling test", { - P= gen_cube(10, 'H') - res = runsample(P, 'H-cube10', distribution) - expect_equal(res, 1) - }) - - test_that("Sampling test", { - P = gen_cross(10, 'H') - res = runsample(P, 'H-cross10', distribution) - expect_equal(res, 1) - }) - - test_that("Sampling test", { - P = gen_prod_simplex(5) - res = runsample(P, 'H-prod_simplex_5_5', distribution) - expect_equal(res, 1) - }) - - test_that("Sampling test", { - P = gen_prod_simplex(10) - res = runsample(P, 'H-prod_simplex_10_10', distribution) - expect_equal(res, 1) - }) - - test_that("Sampling test", { - P = gen_simplex(10, 'H') - res = runsample(P, 'H-prod_simplex10', distribution) - expect_equal(res, 1) - }) - - test_that("Sampling test", { - P = gen_skinny_cube(10) - res = runsample(P, 'H-skinny_cube10', distribution) - expect_equal(res, 1) - }) - - test_that("Sampling test", { - P = gen_skinny_cube(20) - res = runsample(P, 'H-skinny_cube20', distribution) - expect_equal(res, 1) - }) - - test_that("Sampling test", { - Z = gen_rand_zonotope(4, 8) - res = runsample(Z, 'zonotope_4_8', distribution) - expect_equal(res, 1) - }) - -} diff --git a/R-proj/vignettes/Arxiv.Rnw b/R-proj/vignettes/Arxiv.Rnw deleted file mode 100644 index f6c506299..000000000 --- a/R-proj/vignettes/Arxiv.Rnw +++ /dev/null @@ -1,7 +0,0 @@ -\documentclass{article} -\usepackage{pdfpages} -%\VignetteIndexEntry{Arxiv paper} - -\begin{document} -\includepdf[pages=-, fitpaper=true]{volesti_arxiv.pdf} -\end{document} diff --git 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zZ41Zq?sBSW^XmW&oNj>WwT`X89c8zm7chl3Rq7O)%oqq-ofbQ%ihcB-gLG%pBzl){RN(IymP(5qCcPfmF^mP zdi-*w`!zU>T)xvhU#-sdC-c#tFP6V9?Dmat*U0?z+-~I1oZ)$$zxa!fzk1U7e*jIa BQWyXL diff --git a/R-proj/volesti.Rproj b/R-proj/volesti.Rproj deleted file mode 100644 index 21a4da087..000000000 --- a/R-proj/volesti.Rproj +++ /dev/null @@ -1,17 +0,0 @@ -Version: 1.0 - -RestoreWorkspace: Default -SaveWorkspace: Default -AlwaysSaveHistory: Default - -EnableCodeIndexing: Yes -UseSpacesForTab: Yes -NumSpacesForTab: 2 -Encoding: UTF-8 - -RnwWeave: Sweave -LaTeX: pdfLaTeX - -BuildType: Package -PackageUseDevtools: Yes -PackageInstallArgs: --no-multiarch --with-keep.source diff --git a/cran_gen/.Rbuildignore b/cran_gen/.Rbuildignore deleted file mode 100644 index fd2ed1b3d..000000000 --- a/cran_gen/.Rbuildignore +++ /dev/null @@ -1,4 +0,0 @@ -^.*\.Rproj$ -^\.Rproj\.user$ -^volesti\.Rproj$ -^inst/checks$ diff --git a/cran_gen/Makefile b/cran_gen/Makefile deleted file mode 100644 index 3f53931a7..000000000 --- a/cran_gen/Makefile +++ /dev/null @@ -1,23 +0,0 @@ -LP_SOLVE_CPPFLAGS=$(CPPFLAGS) -I../../headers/include \ - -I$(R_INCLUDE_DIR) \ - -DYY_NEVER_INTERACTIVE \ - -DPARSER_LP -DINVERSE_ACTIVE=INVERSE_LUSOL \ - -DRoleIsExternalInvEngine - -LP_SOLVE_SOURCES=colamd.c lp_MDO.c lp_mipbb.c lp_rlp.c mmio.c commonlib.c \ - lp_MPS.c lp_params.c lp_scale.c lp_SOS.c \ - lp_presolve.c lp_simplex.c yacc_read.c ini.c lp_crash.c \ - lp_price.c lp_utils.c lp_Hash.c lp_lib.c lp_pricePSE.c \ - lp_wlp.c lp_LUSOL.c lp_matrix.c lp_report.c lusol.c \ - myblas.c - -LP_SOLVE_OBJECTS=$(LP_SOLVE_SOURCES:.c=.o) - -liblp_solve.a: $(LP_SOLVE_OBJECTS) - $(AR) rc liblp_solve.a $(LP_SOLVE_OBJECTS) && $(RANLIB) liblp_solve.a - -.c.o: - $(CC) $(CFLAGS) $(CPICFLAGS) $(LP_SOLVE_CPPFLAGS) -c $< -o $@ - -clean: - rm -rf $(LP_SOLVE_OBJECTS) liblp_solve.a diff --git a/cran_gen/Makevars b/cran_gen/Makevars deleted file mode 100644 index 8194d4c8a..000000000 --- a/cran_gen/Makevars +++ /dev/null @@ -1,16 +0,0 @@ -PKG_CPPFLAGS=-Iexternal -Iexternal/lpsolve/headers/run_headers -Iexternal/minimum_ellipsoid -Iinclude -Iinclude/convex_bodies/spectrahedra -PKG_CXXFLAGS= -DBOOST_NO_AUTO_PTR -DDISABLE_NLP_ORACLES - -PKG_LIBS=-Lexternal/lpsolve/build/lp_solve -llp_solve -Lexternal/PackedCSparse/qd -lqd $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) - -$(SHLIB): external/lpsolve/build/lp_solve/liblp_solve.a external/PackedCSparse/qd/libqd.a - -external/lpsolve/build/lp_solve/liblp_solve.a: - @(cd external/lpsolve/build/lp_solve && $(MAKE) liblp_solve.a \ - CC="$(CC)" CPPFLAGS="$(CPPFLAGS)" CFLAGS="$(CFLAGS)" \ - CPICFLAGS="$(CPICFLAGS)" AR="$(AR)" RANLIB="$(RANLIB)") - -external/PackedCSparse/qd/libqd.a: - @(cd external/PackedCSparse/qd/ && $(MAKE) libqd.a \ - CC="$(CC)" CPPFLAGS="$(CPPFLAGS)" CFLAGS="$(CFLAGS)" \ - CPICFLAGS="$(CPICFLAGS)" AR="$(AR)") diff --git a/cran_gen/Makevars.win b/cran_gen/Makevars.win deleted file mode 100644 index e4f79d991..000000000 --- a/cran_gen/Makevars.win +++ /dev/null @@ -1,17 +0,0 @@ -PKG_CPPFLAGS=-Iexternal -Iexternal/lpsolve/headers/run_headers -Iexternal/minimum_ellipsoid -Iinclude -Iinclude/convex_bodies/spectrahedra -PKG_CXXFLAGS= -lm -ldl -DBOOST_NO_AUTO_PTR -DDISABLE_NLP_ORACLES - -PKG_LIBS=-Lexternal/lpsolve/build/lp_solve -llp_solve -Lexternal/PackedCSparse/qd -lqd $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) - -$(SHLIB): external/lpsolve/build/lp_solve/liblp_solve.a external/PackedCSparse/qd/libqd.a - -external/lpsolve/build/lp_solve/liblp_solve.a: - @(cd external/lpsolve/build/lp_solve && $(MAKE) liblp_solve.a \ - CC="$(CC)" CPPFLAGS="$(CPPFLAGS) -DUSRDLL -DINLINE=static" \ - CFLAGS="$(CFLAGS)" CPICFLAGS="$(CPICFLAGS)" AR="$(AR)" \ - RANLIB="$(RANLIB)") - -external/PackedCSparse/qd/libqd.a: - @(cd external/PackedCSparse/qd/ && $(MAKE) libqd.a \ - CC="$(CC)" CPPFLAGS="$(CPPFLAGS)" CFLAGS="$(CFLAGS)" \ - CPICFLAGS="$(CPICFLAGS)" AR="$(AR)") diff --git a/cran_gen/NEWS.md b/cran_gen/NEWS.md deleted file mode 100644 index 5038f653b..000000000 --- a/cran_gen/NEWS.md +++ /dev/null @@ -1,31 +0,0 @@ -# volesti 1.0.0 - -* This is the first release of volesti R-Package. - -# volesti 1.0.1 - -* Fix some bugs for solaris os. - -# volesti 1.0.2 - -* Remove r-striper to avoid CRAN policy violation. - -# volesti 1.0.3 - -* Fix CRAN warnings. - -# volesti 1.1.0 - -* New volume computation algorithm. -* Billiard walk for uniform sampling. -* Modified exact volume computation function. -* Implementation and evaluation of PCA method for zonotope approximation. -* Boundary sampling. -* Improved functionality for finance applications. -* Improved names for functions and input variables. -* Use exclusively Eigen/BH library for linear algebra. - -# volesti 1.1.1 - -* Fix CRAN warnings about deprecated use of ftime - diff --git a/cran_gen/README.md b/cran_gen/README.md deleted file mode 100644 index 8a9245ec5..000000000 --- a/cran_gen/README.md +++ /dev/null @@ -1,35 +0,0 @@ -# Volume computation and sampling - -## About -The `volesti` package provides [R](https://www.r-project.org/) with functions for volume estimation and sampling. In particular, it provides an R interface for the C++ library [**volesti**](https://github.com/GeomScale/volume_approximation). - -`volesti` computes approximations of volume of polytopes given as a set of points or linear inequalities or as a Minkowski sum of segments (zonotopes). There are algorithms for volume approximation as well as algorithms for sampling, rounding and rotating polytopes. Last but not least, `volesti` provides implementations of geometric algorithms to compute the score of a portfolio given asset returns and to detect financial crises in stock markets. - -## Download and install - -* The latest stable version is available from CRAN. -* The latest development version is available on Github `www.github.com/GeomScale/volume_approximation` - -* Install `volesti` by running: -``` -install.packages("volesti") -``` -* The package-dependencies are: `Rcpp`, `RcppEigen`, `BH`. - -## Documentation - -* [Using the R Interface](https://github.com/GeomScale/volume_approximation/blob/v1.1.1/doc/r_interface.md) -* [Wikipage with Tutorials and Demos](https://github.com/GeomScale/volume_approximation/wiki) -* [Tutorial given to PyData meetup](https://vissarion.github.io/tutorials/volesti_tutorial_pydata.html) - - -## Credits - -* [Contributors and Package History](https://github.com/GeomScale/volume_approximation/blob/v1.1.1/doc/credits.md) -* [List of Publications](https://github.com/GeomScale/volume_approximation/blob/v1.1.1/doc/publications.md) - -Copyright (c) 2012-2020 Vissarion Fisikopoulos -Copyright (c) 2018-2020 Apostolos Chalkis - -You may redistribute or modify the software under the GNU Lesser General Public License as published by Free Software Foundation, either version 3 of the License, or (at your option) any later version. It is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY. - diff --git a/cran_gen/genCRANpkg.R b/cran_gen/genCRANpkg.R deleted file mode 100644 index abf51d88a..000000000 --- a/cran_gen/genCRANpkg.R +++ /dev/null @@ -1,186 +0,0 @@ -# create cran package folder -dir.create("cran_package") -# set /root as main path -path = getwd() -path = substr(path, start=1, stop=nchar(path)-9) - -# download lpSolveAPI and crate src/Rproj_externals/lp_solve -library(downloader) -download("https://cran.r-project.org/src/contrib/Archive/lpSolveAPI/lpSolveAPI_5.5.2.0-17.6.tar.gz", dest="lpSolve.tar.gz", mode="wb") -untar("lpSolve.tar.gz", exdir = path) -unlink("lpSolve.tar.gz") -dir.create(paste0(path,"/R-proj/src/Rproj_externals")) -dir_lp = paste0(path,"/lpSolveAPI/src/lp_solve") -lp_dist = paste0(path,"/R-proj/src/Rproj_externals") -file.copy(dir_lp, lp_dist, recursive=TRUE) - -# fix ftime deprecation, taken from: https://github.com/GeomScale/volesti/pull/89/files -library(xfun) -gsub_file( - paste0(path,"/R-proj/src/Rproj_externals/lp_solve/commonlib.c"), - "struct timeb buf;", "", - fixed=TRUE) -gsub_file( - paste0(path,"/R-proj/src/Rproj_externals/lp_solve/commonlib.c"), - "ftime(&buf);", "", - fixed=TRUE) -gsub_file( - paste0(path,"/R-proj/src/Rproj_externals/lp_solve/commonlib.c"), - "return((double)buf.time+((double) buf.millitm)/1000.0);", - "return((double)0);", - fixed=TRUE) - -# add lpsolve header files in external -library(downloader) -download("https://cran.r-project.org/src/contrib/Archive/lpSolve/lpSolve_5.6.15.tar.gz", dest="lpSolve.tar.gz", mode="wb") -untar("lpSolve.tar.gz", exdir = path) -unlink("lpSolve.tar.gz") -dir.create(paste0(path,"/external/LPsolve_src")) -dir.create(paste0(path,"/external/LPsolve_src/include")) -dir.create(paste0(path,"/external/LPsolve_src/run_headers")) -dir_lp = paste0(path,"/lpSolveAPI/inst/include") -h_files = dir(dir_lp, "*.h", ignore.case = TRUE, all.files = TRUE) -lp_dist = paste0(path,"/external/LPsolve_src/include") -file.copy(file.path(dir_lp, h_files), lp_dist, recursive=TRUE, overwrite=TRUE) -# replace the lpsolve header file that issues a warning in mac's cran test -file.copy(paste0(path,"/external/cmake-files/lpsolve_modified_header_files/lp_rlp.h"), lp_dist, recursive=TRUE, overwrite=TRUE) -dir_lp = paste0(path,"/lpSolve/src") -h_files = dir(dir_lp, "*.h", ignore.case = TRUE, all.files = TRUE) -lp_dist = paste0(path,"/external/LPsolve_src/run_headers") -file.copy(file.path(dir_lp, h_files), lp_dist, recursive=TRUE) -unlink(paste0(path,"/lpSolveAPI"), recursive=TRUE) -unlink(paste0(path,"/lpSolve"), recursive=TRUE) - -# copy paste the src folder -src_dir = paste0(path,'/R-proj/src') -src_dist = paste0(path,'/cran_gen/cran_package') -file.copy(src_dir, src_dist, recursive=TRUE) - -# copy paste the inst folder -inst_dir = paste0(path,'/R-proj/inst') -inst_dist = paste0(path,'/cran_gen/cran_package') -file.copy(inst_dir, inst_dist, recursive=TRUE) - -# copy paste the man folder -man_dir = paste0(path,'/R-proj/man') -man_dist = paste0(path,'/cran_gen/cran_package') -file.copy(man_dir, man_dist, recursive=TRUE) - -# copy paste the R folder -R_dir = paste0(path,'/R-proj/R') -R_dist = paste0(path,'/cran_gen/cran_package') -file.copy(R_dir, R_dist, recursive=TRUE) - -# copy paste the tests folder -tests_dir = paste0(path,'/R-proj/tests') -tests_dist = paste0(path,'/cran_gen/cran_package') -file.copy(tests_dir, tests_dist, recursive=TRUE) - -# copy paste the DESCRIPTION file -descr_dir = paste0(path,'/R-proj/DESCRIPTION') -descr_dist = paste0(path,'/cran_gen/cran_package') -file.copy(descr_dir, descr_dist, recursive=TRUE) - -# copy paste the NAMESPACE file -namesp_dir = paste0(path,'/R-proj/NAMESPACE') -namesp_dist = paste0(path,'/cran_gen/cran_package') -file.copy(namesp_dir, namesp_dist, recursive=TRUE) - -# copy paste the volesti.Rproj -volpro_dir = paste0(path,'/R-proj/volesti.Rproj') -volpro_dist = paste0(path,'/cran_gen/cran_package') -file.copy(volpro_dir, volpro_dist, recursive=TRUE) - -# copy paste the external folder -external_dir = paste0(path,'/external') -external_dist = paste0(path,'/cran_gen/cran_package/src') -file.copy(external_dir, external_dist, recursive=TRUE) - -# copy paste the include folder -include_dir = paste0(path,'/include') -include_dist = paste0(path,'/cran_gen/cran_package/src') -file.copy(include_dir, include_dist, recursive=TRUE) - -# copy paste the README.md file -readme_dir = paste0(path,'/cran_gen/README.md') -readme_dist = paste0(path,'/cran_gen/cran_package') -file.copy(readme_dir, readme_dist, recursive=TRUE) - -# copy paste the NEWS.md file -news_dir = paste0(path,'/cran_gen/NEWS.md') -news_dist = paste0(path,'/cran_gen/cran_package') -file.copy(news_dir, news_dist, recursive=TRUE) - -# copy paste the cran-comments.md file -#cran_com_dir = paste0(path,'/cran_gen/cran-comments.md') -#cran_com_dist = paste0(path,'/cran_gen/cran_package') -#file.copy(cran_com_dir, cran_com_dist, recursive=TRUE) - -# copy paste the Rbuildignore.md file -Rbuild_dir = paste0(path,'/cran_gen/.Rbuildignore') -Rbuild_dist = paste0(path,'/cran_gen/cran_package') -file.copy(Rbuild_dir, Rbuild_dist, recursive=TRUE) - -# replace the Makevars -makevars_dir = paste0(path,'/cran_gen/Makevars') -makevars_dist = paste0(path, '/cran_gen/cran_package/src') -file.copy(makevars_dir, makevars_dist, recursive=TRUE) - -# replace the Makevars.win -makevarswin_dir = paste0(path,'/cran_gen/Makevars.win') -makevarswin_dist = paste0(path, '/cran_gen/cran_package/src') -file.copy(makevarswin_dir, makevarswin_dist, recursive=TRUE) - -# copy paste the LICENCE -dir_lic = paste0(path,'/LICENSE') -lic_dist = paste0(path,'/cran_package/inst/doc') - -# delete misc.h from include -dir_misc = paste0(path,'/cran_gen/cran_package/src/include/misc.h') -unlink(dir_misc, recursive = TRUE) - -# delete linear_extensions.h from include -dir_lin_ext = paste0(path,'/cran_gen/cran_package/src/include/linear_extensions.h') -unlink(dir_lin_ext, recursive = TRUE) - -# create lpsolve folder -dir.create(paste0(path,"/cran_gen/cran_package/src/external/lpsolve")) -dir.create(paste0(path,"/cran_gen/cran_package/src/external/lpsolve/build")) -dir.create(paste0(path,"/cran_gen/cran_package/src/external/lpsolve/headers")) -dir_lp = paste0(path,"/cran_gen/cran_package/src/Rproj_externals/lp_solve") -lp_dist = (paste0(path,"/cran_gen/cran_package/src/external/lpsolve/build")) -file.copy(dir_lp, lp_dist, recursive=TRUE) -dir_lp = paste0(path,"/cran_gen/cran_package/src/external/LPsolve_src/include") -lp_dist = (paste0(path,"/cran_gen/cran_package/src/external/lpsolve/headers")) -file.copy(dir_lp, lp_dist, recursive=TRUE) -dir_lp = paste0(path,"/cran_gen/cran_package/src/external/LPsolve_src/run_headers") -lp_dist = (paste0(path,"/cran_gen/cran_package/src/external/lpsolve/headers")) -file.copy(dir_lp, lp_dist, recursive=TRUE) -dir_lpsolve_heds = paste0(path,"/cran_gen/cran_package/src/external/LPsolve_src") -unlink(dir_lpsolve_heds, recursive = TRUE) -dir_lpsolve_heds = paste0(path,"/cran_gen/cran_package/src/Rproj_externals") -unlink(dir_lpsolve_heds, recursive = TRUE) - -# replace the Makefile -makefile_dir = paste0(path,'/cran_gen/Makefile') -makefile_dist = paste0(path, '/cran_gen/cran_package/src/external/lpsolve/build/lp_solve') -file.copy(makefile_dir, makefile_dist, recursive=TRUE) - - - -# set new cran package folder as wrking directory -setwd(paste0(path,'/cran_gen/cran_package')) -# enable devtools and Rcpp libraries -library(devtools) -library(Rcpp) - -# build package tar.gz -Rcpp::compileAttributes() -devtools::build() - -# set /root/R-proj as the working directory -setwd(paste0(path,'/R-proj')) - -# delete folder cran_package -#dir_cr_pkg = paste0(path,'/cran_gen/cran_package') -#unlink(dir_cr_pkg, recursive = TRUE) diff --git a/include/ode_solvers/oracle_functors_rcpp.hpp b/include/ode_solvers/oracle_functors_rcpp.hpp deleted file mode 100644 index 853dcb3c0..000000000 --- a/include/ode_solvers/oracle_functors_rcpp.hpp +++ /dev/null @@ -1,159 +0,0 @@ -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2020 Vissarion Fisikopoulos -// Copyright (c) 2018-2020 Apostolos Chalkis -// Copyright (c) 2020-2020 Marios Papachristou - -// Contributed and/or modified by Marios Papachristou, as part of Google Summer of Code 2020 program. - -// Licensed under GNU LGPL.3, see LICENCE file - -#ifndef ODE_SOLVERS_ORACLE_FUNCTORS_RCPP_HPP -#define ODE_SOLVERS_ORACLE_FUNCTORS_RCPP_HPP - -enum ode_solvers { - no_solver, - leapfrog, - euler, - runge_kutta, - richardson, - collocation -}; - -// Holds Oracle Functor that wraps an R function via RCpp -// The R function is provided as an Rcpp::Function object -// The functor uses Rcpp::as and Rcpp::wrap to do the conversion, -// call the oracle, and convert the results back to C++ -struct RcppFunctor { - - template < - typename NT - > - struct parameters { - NT L; // Lipschitz constant of gradient - NT m; // Strong-convexity parameter - NT eta; // Step-size (if defined by user) - NT kappa; // Condition number - unsigned int order; // Order of ODE functor - - parameters( - NT L_, - NT m_, - NT eta_, - unsigned int order_=2 - ) : - L(L_), - m(m_), - eta(eta_), - kappa(L_ / m_), - order(order_) - {} - }; - - // Log-probability gradient functor - template - < - typename Point - > - struct GradientFunctor { - typedef typename Point::FT NT; - typedef typename Point::Coeff VT; - typedef std::vector pts; - - parameters params; - Rcpp::Function neg_grad_f; // Negative gradient as an Rcpp::Function - bool negate; - - GradientFunctor( - parameters params_, - Rcpp::Function neg_grad_f_, - bool negate_=true): - params(params_), - neg_grad_f(neg_grad_f_), - negate(negate_) - {}; - - // The index i represents the state vector index - Point operator() (unsigned int const& i, pts const& xs, NT const& t) const { - if (i == params.order - 1) { - // Convert point to Rcpp::NumericMatrix - - VT y = Rcpp::as(neg_grad_f(Rcpp::wrap(xs[0].getCoefficients()))); - - Point z(y); - - if (negate) z = (-1.0) * z; - - // Return result as Point - return z; - } else { - return xs[i + 1]; // returns derivative - } - } - - Point operator() (Point const& x) const { - VT y = Rcpp::as(neg_grad_f(Rcpp::wrap(x.getCoefficients()))); - - Point z(y); - - if (negate) z = (-1.0) * z; - - // Return result as Point - return z; - } - - }; - - // Negative log-probability functor - template - < - typename Point - > - struct FunctionFunctor { - typedef typename Point::FT NT; - typedef typename Point::Coeff VT; - - parameters params; - Rcpp::Function negative_logprob; - - FunctionFunctor( - parameters params_, - Rcpp::Function negative_logprob_) : - params(params_), - negative_logprob(negative_logprob_) - {}; - - NT operator() (Point const& x) const { - return Rcpp::as(negative_logprob(Rcpp::wrap(x.getCoefficients()))); - } - - }; - - // Log-probability hessian functor - template - < - typename Point - > - struct HessianFunctor { - typedef typename Point::FT NT; - typedef typename Point::Coeff VT; - - parameters params; - Rcpp::Function hessian; // Negative hessian as an Rcpp::Function - - HessianFunctor( - parameters params_, - Rcpp::Function hessian_) : - params(params_), - hessian(hessian_) - {}; - - Point operator() (Point const& x) const { - VT y= Rcpp::as(hessian(Rcpp::wrap(x.getCoefficients()))); - return Point(y); - } - - }; -}; - -#endif From 8724a7301984bcc8ffcc418ea0d618dddee2c6b9 Mon Sep 17 00:00:00 2001 From: vfisikop Date: Wed, 6 Mar 2024 13:19:28 +0200 Subject: [PATCH 18/35] Fix includes of header files --- test/crhmc_sampling_test.cpp | 2 +- test/sampling_correlation_matrices_test.cpp | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/test/crhmc_sampling_test.cpp b/test/crhmc_sampling_test.cpp index a13a6e299..b762ea7b5 100644 --- a/test/crhmc_sampling_test.cpp +++ b/test/crhmc_sampling_test.cpp @@ -13,7 +13,7 @@ #include "doctest.h" #include "generators/known_polytope_generators.h" #include "misc/misc.h" -#include "ode_solvers.hpp" +#include "ode_solvers/ode_solvers.hpp" #include "preprocess/crhmc/crhmc_input.h" #include "preprocess/crhmc/crhmc_problem.h" #include "preprocess/crhmc/crhmc_problem.h" diff --git a/test/sampling_correlation_matrices_test.cpp b/test/sampling_correlation_matrices_test.cpp index f2f90c529..39bb0e605 100644 --- a/test/sampling_correlation_matrices_test.cpp +++ b/test/sampling_correlation_matrices_test.cpp @@ -9,18 +9,18 @@ #include "doctest.h" #include -#include "misc.h" + #include "random.hpp" #include "random/uniform_int.hpp" #include "random/normal_distribution.hpp" #include "random/uniform_real_distribution.hpp" #include "cartesian_geom/cartesian_kernel.h" +#include "diagnostics/univariate_psrf.hpp" +#include "misc/misc.h" #include "random_walks/random_walks.hpp" #include "sampling/sample_correlation_matrices.hpp" -#include "diagnostics/univariate_psrf.hpp" - template MT rebuildMatrix(const VT &xvector, const unsigned int n){ MT mat = MT::Identity(n,n); From 4c6bcc3e570befc7d6851e5f62561a73a601ec71 Mon Sep 17 00:00:00 2001 From: vfisikop Date: Wed, 6 Mar 2024 13:54:02 +0200 Subject: [PATCH 19/35] Fix windows CI tests --- .github/workflows/R-CMD-check-windows.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/R-CMD-check-windows.yml b/.github/workflows/R-CMD-check-windows.yml index e5c1cf7b3..97e725aa0 100644 --- a/.github/workflows/R-CMD-check-windows.yml +++ b/.github/workflows/R-CMD-check-windows.yml @@ -44,8 +44,8 @@ jobs: run: git clone https://github.com/GeomScale/Rvolesti.git; - name: Upgrade with current develop of volesti - run: cp -rf include Rvolesti/src/volesti; - cp -rf external Rvolesti/src; + run: cp -r -fo include Rvolesti/src/volesti; + cp -r -fo external Rvolesti/src; - name: Check env: From 4ee5374c9206f7411f61d2d89ba73522dab3bdb9 Mon Sep 17 00:00:00 2001 From: vfisikop Date: Thu, 7 Mar 2024 23:14:30 +0200 Subject: [PATCH 20/35] Remove unused header files --- test/crhmc_sampling_test.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/test/crhmc_sampling_test.cpp b/test/crhmc_sampling_test.cpp index b762ea7b5..79697b4a4 100644 --- a/test/crhmc_sampling_test.cpp +++ b/test/crhmc_sampling_test.cpp @@ -13,7 +13,6 @@ #include "doctest.h" #include "generators/known_polytope_generators.h" #include "misc/misc.h" -#include "ode_solvers/ode_solvers.hpp" #include "preprocess/crhmc/crhmc_input.h" #include "preprocess/crhmc/crhmc_problem.h" #include "preprocess/crhmc/crhmc_problem.h" From 9114f37b4a3cc3838a9bbdc64118d136be080d70 Mon Sep 17 00:00:00 2001 From: vfisikop Date: Fri, 15 Mar 2024 15:00:10 +0200 Subject: [PATCH 21/35] Update and simplify documentation --- docs/getting_started/cpp_example.md | 47 +++++++---------------------- docs/getting_started/install.md | 45 ++------------------------- docs/getting_started/r_example.md | 7 ----- docs/index.rst | 1 - docs/misc/documentation.md | 20 ------------ 5 files changed, 14 insertions(+), 106 deletions(-) delete mode 100644 docs/getting_started/r_example.md delete mode 100644 docs/misc/documentation.md diff --git a/docs/getting_started/cpp_example.md b/docs/getting_started/cpp_example.md index 82ac82902..e3e39dcbf 100644 --- a/docs/getting_started/cpp_example.md +++ b/docs/getting_started/cpp_example.md @@ -5,7 +5,6 @@ Here we give step-by-step instructions for how to estimate the volume of a 3-dim Write the following C++ code and save it in `volume_example.cpp` ```c++ - #include "Eigen/Eigen" #include "cartesian_geom/cartesian_kernel.h" #include "convex_bodies/hpolytope.h" @@ -30,8 +29,7 @@ int main() { double e = 0.1; // Calculating volume of the passed polytope - double volume = volume_cooling_balls - (HP, e, walk_len).second; + double volume = volume_cooling_balls(HP, e, walk_len).second; std::cout << "Volume of the cube: " << volume << std::endl; @@ -46,18 +44,15 @@ project( VolEsti-cpp-example ) CMAKE_MINIMUM_REQUIRED(VERSION 3.11) -set(CMAKE_ALLOW_LOOSE_LOOP_CONSTRUCTS true) - -if(COMMAND cmake_policy) - cmake_policy(SET CMP0003 NEW) -endif(COMMAND cmake_policy) - add_definitions(-DDISABLE_NLP_ORACLES) -option(BUILTIN_EIGEN "Use eigen from ../../external" OFF) - include("../../external/cmake-files/Eigen.cmake") GetEigen() +if (${CMAKE_VERSION} VERSION_LESS "3.12.0") + add_compile_options(-D "EIGEN_NO_DEBUG") +else () + add_compile_definitions("EIGEN_NO_DEBUG") +endif () include("../../external/cmake-files/Boost.cmake") GetBoost() @@ -65,33 +60,13 @@ GetBoost() include("../../external/cmake-files/LPSolve.cmake") GetLPSolve() -# Find lpsolve library -find_library(LP_SOLVE NAMES liblpsolve55.so PATHS /usr/lib/lp_solve) - -if (NOT LP_SOLVE) - message(FATAL_ERROR "This program requires the lp_solve library, and will not be compiled.") -else () - message(STATUS "Library lp_solve found: ${LP_SOLVE}") - - set(CMAKE_EXPORT_COMPILE_COMMANDS "ON") - - include_directories (BEFORE ../../external) - include_directories (BEFORE ../../include) - - # for Eigen - if (${CMAKE_VERSION} VERSION_LESS "3.12.0") - add_compile_options(-D "EIGEN_NO_DEBUG") - else () - add_compile_definitions("EIGEN_NO_DEBUG") - endif () - - - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard +set(CMAKE_EXPORT_COMPILE_COMMANDS "ON") - add_executable (volume_example volume_example.cpp) - TARGET_LINK_LIBRARIES(volume_example ${LP_SOLVE}) +include_directories (BEFORE ../../external) +include_directories (BEFORE ../../include) -endif() +add_executable (volume_example volume_example.cpp) +target_link_libraries(volume_example PUBLIC lp_solve) ``` We will use `cmake` to build the makefile and compile our example. diff --git a/docs/getting_started/install.md b/docs/getting_started/install.md index cfdb773e7..58fcfe163 100644 --- a/docs/getting_started/install.md +++ b/docs/getting_started/install.md @@ -6,16 +6,13 @@ Installation Guide ### Compile C++ sources and run tests --- -To compile the C++ code you need the [lp_solve](http://lpsolve.sourceforge.net/5.5/) library. For example, for Unix/Linux you need `liblpsolve55.so`. This is available from the library's [webpage](http://lpsolve.sourceforge.net/5.5/) as well as a package in several linux distributions e.g. [debian](https://packages.debian.org/stretch/liblpsolve55-dev) `sudo apt-get install lp-solve`. - -You have to specify the path to `liblpsolve55.so/dll/dylib`, by running, in folder test: +To compile the tests of the C++ interface simply ```bash mkdir -p test/build && cd test/build -cmake -DLP_SOLVE=_PATH_TO_LIB_FILE_ .. +cmake .. make ``` -For example: `-DLP_SOLVE=/usr/lib/lpsolve/liblpsolve55.so` In WSL (Windows Subsystem Linux), you can run the following command to install libc6-dev-i386. This will be required for `ieeefp.h` which is used by `qd` library, @@ -59,43 +56,7 @@ docker run -it -v $PWD:/volesti -w /volesti --name=volesti-dev volesti:dev /bin/ ## R Interface -### Install Rcpp package ---- - -1. Install package-dependencies: ``Rcpp``, ``RcppEigen``, ``BH``. - -2. Then use ``devtools`` package to install ``volesti`` Rcpp package. From terminal go to folder ``/root/R-proj`` and run in terminal: - -```bash - Rscript -e 'Rcpp::compileAttributes()' - R CMD INSTALL --no-multiarch --with-keep.source . -``` - -3. You can use Rstudio as well to open ``volesti.Rproj`` and then click `build source Package` and then `Install and Restart` in `Build` at the menu bar. - -### Generate CRAN version ---- - -To generate the CRAN version of the R package follow the instructions below: - -1. From the command line navigate to folder ``/cran_gen``. Then Run: - -```r - source('genCRANpkg.R') -``` - -2. Open ``genCRANpkg.R`` script with `Rstudio` and run it. - -### Run volesti from R ---- - -* The main function is ``volume()``. It can be used to approximate the volume of a convex polytope given as a set of linear inequalities or a set of vertices (d-dimensional points) or as a Minkowski sum of segments (zonotope). There are three algorithms that can be used (``SequenceOfBalls``, ``CoolingGaussian`` and ``CoolingBalls``). -* The function ``sample_points()`` can be used to sample points from a convex polytope with uniform or spherical gaussian target distribution. -* The function ``round_polytope()`` can be used to round a convex polytope. -* The function ``rand_rotate()`` can be used to apply a random rotation to a convex polytope. - -For more details you can read the documentation in folder ``/inst/doc``. - +An ``R`` interface is available from the package [Rvolesti](https://github.com/GeomScale/Rvolesti). ## Python Interface diff --git a/docs/getting_started/r_example.md b/docs/getting_started/r_example.md deleted file mode 100644 index 0850de823..000000000 --- a/docs/getting_started/r_example.md +++ /dev/null @@ -1,7 +0,0 @@ -# How to create your first example using the R interface - -Here we give step-by-step instructions for how to estimate the volume of a 3-dimensional cube using the R interface of the `volesti` library. - -The user first should install the R interface by following instructions in {doc}`installation guide `. - -to be written ... diff --git a/docs/index.rst b/docs/index.rst index a64b5bf4b..ce0b73886 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -12,7 +12,6 @@ Welcome to VolEsti's documentation! getting_started/install.md getting_started/cpp_example.md - getting_started/r_example.md .. toctree:: :caption: Tutorials diff --git a/docs/misc/documentation.md b/docs/misc/documentation.md deleted file mode 100644 index 35e066604..000000000 --- a/docs/misc/documentation.md +++ /dev/null @@ -1,20 +0,0 @@ -# Generate documentation - -## How to generate documentation - -Technologies used: Doxygen + Spnynx + Breathe - -to be written ... - -## Create pdf documentation from Rd files - -* Install volesti library. -* In `R` mode (or in Rstudio) Run -```r -pack = "volesti" -path = find.package(pack) -system(paste(shQuote(file.path(R.home("bin"), "R")), - "CMD", "Rd2pdf", shQuote(path))) -``` -* The pdf will be created and saved in `R-proj` folder. -* We give such a documentation in `/R-proj/doc` folder. From 742eb01507c85639e748be12673d4fde3b5e47f7 Mon Sep 17 00:00:00 2001 From: vfisikop Date: Thu, 28 Mar 2024 12:50:26 +0200 Subject: [PATCH 22/35] Refactor trigonometric_positive_intersect function for hpolytopes --- include/convex_bodies/hpolytope.h | 41 +++++++++++++++++-------------- 1 file changed, 22 insertions(+), 19 deletions(-) diff --git a/include/convex_bodies/hpolytope.h b/include/convex_bodies/hpolytope.h index 49d942827..bee0fd369 100644 --- a/include/convex_bodies/hpolytope.h +++ b/include/convex_bodies/hpolytope.h @@ -750,22 +750,20 @@ class HPolytope { } - //------------oracle for exact hmc spherical gaussian sampling---------------// - + // Boundary oracle for exact hmc spherical gaussian sampling // compute intersection point of ray starting from r and pointing to v - // with polytope discribed by A and b + // with polytope discribed by A and b (the ray describes a curve) std::pair trigonometric_positive_intersect(Point const& r, Point const& v, NT const& omega, int &facet_prev) const { + constexpr NT pi_2 = NT(2.0) * M_PI; + NT t = std::numeric_limits::max(); - NT lamda = 0, C, Phi, t1, t2, tmin; - NT min_plus = std::numeric_limits::max(), t = std::numeric_limits::max(); - NT max_minus = std::numeric_limits::lowest(); - VT sum_nom, sum_denom; - unsigned int j; - int m = num_of_hyperplanes(), facet = -1; - + int m = num_of_hyperplanes(); + int facet = -1; + VT sum_nom; + VT sum_denom; sum_nom.noalias() = A * r.getCoefficients(); sum_denom.noalias() = A * v.getCoefficients(); @@ -773,10 +771,15 @@ class HPolytope { NT* sum_denom_data = sum_denom.data(); const NT* b_data = b.data(); + const NT omega_sqr = omega * omega; + const NT pi_2_omega = pi_2 / omega; + for (int i = 0; i < m; i++) { - C = std::sqrt((*sum_nom_data) * (*sum_nom_data) + ((*sum_denom_data) * (*sum_denom_data)) / (omega * omega)); - Phi = std::atan((-(*sum_denom_data)) / ((*sum_nom_data) * omega)); + NT C = std::sqrt((*sum_nom_data) * (*sum_nom_data) + ((*sum_denom_data) * (*sum_denom_data)) + / omega_sqr); + NT Phi = std::atan((-(*sum_denom_data)) / ((*sum_nom_data) * omega)); + if ((*sum_denom_data) < 0.0 && Phi < 0.0) { Phi += M_PI; } else if ((*sum_denom_data) > 0.0 && Phi > 0.0) { @@ -785,20 +788,20 @@ class HPolytope { if (C > (*b_data)) { NT acos_b = std::acos((*b_data) / C); - t1 = (acos_b - Phi) / omega; + NT t1 = (acos_b - Phi) / omega; if (facet_prev == i && std::abs(t1) < 1e-10){ - t1 = (2.0 * M_PI) / omega; + t1 = pi_2_omega; } - t2 = (-acos_b - Phi) / omega; + NT t2 = (-acos_b - Phi) / omega; if (facet_prev == i && std::abs(t2) < 1e-10){ - t2 = (2.0 * M_PI) / omega; + t2 = pi_2_omega; } - t1 += (t1 < NT(0)) ? (2.0 * M_PI) / omega : NT(0); - t2 += (t2 < NT(0)) ? (2.0 * M_PI) / omega : NT(0); + t1 += (t1 < NT(0)) ? pi_2_omega : NT(0); + t2 += (t2 < NT(0)) ? pi_2_omega : NT(0); - tmin = std::min(t1, t2); + NT tmin = std::min(t1, t2); if (tmin < t && tmin > NT(0)) { facet = i; From 9c32d0b5fd38a8099138fb20169fabfc590f9cc3 Mon Sep 17 00:00:00 2001 From: vgnecula Date: Mon, 10 Jun 2024 22:13:03 -0400 Subject: [PATCH 23/35] update_position complexity improvement --- ...ian_hamiltonian_monte_carlo_exact_walk.hpp | 26 ++++++++++--------- 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/include/random_walks/gaussian_hamiltonian_monte_carlo_exact_walk.hpp b/include/random_walks/gaussian_hamiltonian_monte_carlo_exact_walk.hpp index 8b83b053b..bda33630c 100644 --- a/include/random_walks/gaussian_hamiltonian_monte_carlo_exact_walk.hpp +++ b/include/random_walks/gaussian_hamiltonian_monte_carlo_exact_walk.hpp @@ -226,20 +226,22 @@ private : inline void update_position(Point &p, Point &v, NT const& T, NT const& omega) { - NT C, Phi; - for (size_t i = 0; i < p.dimension(); i++) + NT next_p, next_v; + + NT sinVal = std::sin(omega * T); + NT cosVal = std::cos(omega * T); + + NT factor1 = sinVal / omega; + NT factor2 = -omega * sinVal; + + for (size_t i = 0; i < p.dimension(); i++) { - C = std::sqrt(p[i] * p[i] + (v[i] * v[i]) / (omega * omega)); - Phi = std::atan((-v[i]) / (p[i] * omega)); - if (v[i] < 0.0 && Phi < 0.0) { - Phi += M_PI; - } else if (v[i] > 0.0 && Phi > 0.0) { - Phi -= M_PI; - } - p.set_coord(i, C * std::cos(omega * T + Phi)); - v.set_coord(i, -C * omega * std::sin(omega * T + Phi)); - } + next_p = cosVal * p[i] + v[i] * factor1; + next_v = factor2 * p[i] + cosVal * v[i]; + p.set_coord(i, next_p); + v.set_coord(i, next_v); + } } inline double get_max_distance(std::vector &pointset, Point const& q, double &rad) From e4848659e70f87db00ff60fa1cac4eb37eff13d5 Mon Sep 17 00:00:00 2001 From: atrayees <121290945+atrayees@users.noreply.github.com> Date: Mon, 17 Jun 2024 17:46:41 +0530 Subject: [PATCH 24/35] Feature/spectra correlations (#306) --- examples/correlation_matrices/sampler.cpp | 62 +++++++++++++++---- .../matrix_operations/EigenvaluesProblems.h | 43 +++++++++++-- 2 files changed, 88 insertions(+), 17 deletions(-) diff --git a/examples/correlation_matrices/sampler.cpp b/examples/correlation_matrices/sampler.cpp index 9a2ac08d2..f18f90a1b 100644 --- a/examples/correlation_matrices/sampler.cpp +++ b/examples/correlation_matrices/sampler.cpp @@ -17,6 +17,7 @@ #include "convex_bodies/spectrahedra/spectrahedron.h" #include "random_walks/random_walks.hpp" #include "sampling/sample_correlation_matrices.hpp" +#include "matrix_operations/EigenvaluesProblems.h" typedef double NT; typedef Eigen::Matrix MT; @@ -89,6 +90,28 @@ void write_to_file(std::string filename, std::vector const& randPoint std::cout.rdbuf(coutbuf); } +bool is_correlation_matrix(const MT& matrix, const double tol = 1e-8){ + //check if all the diagonal elements are ones + for(int i=0 ; i tol) + { + return false; + } + } + + //check if the matrix is positive semidefinite + using NT = double; + using MatrixType = Eigen::Matrix; + EigenvaluesProblems> solver; + + if(solver.isPositiveSemidefinite(matrix)) + { + return true; + } + return false; +} + template void correlation_matrix_uniform_sampling(const unsigned int n, const unsigned int num_points, std::string walkname){ @@ -106,7 +129,7 @@ void correlation_matrix_uniform_sampling(const unsigned int n, const unsigned in time = std::chrono::duration_cast(end - start).count(); std::cout << "Elapsed time : " << time << " (ms)" << std::endl; - write_to_file(walkname + "_matrices.txt", randPoints); + write_to_file(walkname + "_matrices" + std::to_string(n) + ".txt", randPoints); } template @@ -126,7 +149,15 @@ void correlation_matrix_uniform_sampling_MT(const unsigned int n, const unsigned time = std::chrono::duration_cast(end - start).count(); std::cout << "Elapsed time : " << time << " (ms)" << std::endl; - write_to_file(walkname + "_matrices_MT.txt", randPoints); + int valid_points = 0; + for(const auto& points : randPoints){ + if(is_correlation_matrix(points.mat)){ + valid_points++; + } + } + std::cout << "Number of valid points = " << valid_points << std::endl; + + write_to_file(walkname + "_matrices_MT" + std::to_string(n) + ".txt", randPoints); } int main(int argc, char const *argv[]){ @@ -146,25 +177,30 @@ int main(int argc, char const *argv[]){ printf("\n"); #endif - unsigned int n = 3, num_points = 5000; + unsigned int num_points = 5000; + + std::vector dimensions = {3, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100}; - old_uniform_sampling(n, num_points); + for(unsigned int n : dimensions){ - correlation_matrix_uniform_sampling(n, num_points, "BallWalk"); + old_uniform_sampling(n, num_points); - correlation_matrix_uniform_sampling(n, num_points, "RDHRWalk"); + correlation_matrix_uniform_sampling(n, num_points, "BallWalk"); - correlation_matrix_uniform_sampling(n, num_points, "BilliardWalk"); + correlation_matrix_uniform_sampling(n, num_points, "RDHRWalk"); - correlation_matrix_uniform_sampling(n, num_points, "AcceleratedBilliardWalk"); + correlation_matrix_uniform_sampling(n, num_points, "BilliardWalk"); - correlation_matrix_uniform_sampling_MT(n, num_points, "BallWalk"); + correlation_matrix_uniform_sampling(n, num_points, "AcceleratedBilliardWalk"); - correlation_matrix_uniform_sampling_MT(n, num_points, "RDHRWalk"); + correlation_matrix_uniform_sampling_MT(n, num_points, "BallWalk"); - correlation_matrix_uniform_sampling_MT(n, num_points, "BilliardWalk"); + correlation_matrix_uniform_sampling_MT(n, num_points, "RDHRWalk"); - correlation_matrix_uniform_sampling_MT(n, num_points, "AcceleratedBilliardWalk"); + correlation_matrix_uniform_sampling_MT(n, num_points, "BilliardWalk"); + + correlation_matrix_uniform_sampling_MT(n, num_points, "AcceleratedBilliardWalk"); + } return 0; -} \ No newline at end of file +} diff --git a/include/matrix_operations/EigenvaluesProblems.h b/include/matrix_operations/EigenvaluesProblems.h index 4012177f3..178d91666 100644 --- a/include/matrix_operations/EigenvaluesProblems.h +++ b/include/matrix_operations/EigenvaluesProblems.h @@ -167,9 +167,12 @@ class EigenvaluesProblems, E Spectra::DenseSymMatProd op(B); Spectra::DenseCholesky Bop(-A); - // Construct generalized eigen solver object, requesting the largest three generalized eigenvalues + // Construct generalized eigen solver object, computing the minimum positive eigenvalue by computing the largest eigenvalue of the inverse Generalized Eigenvalue Problem + // An empirical value of ncv that gives a better performance + // TODO: tune this implementation by tuning the parameters like ncv + int ncv = std::min(std::max(10, matrixDim/20), matrixDim); Spectra::SymGEigsSolver, Spectra::DenseCholesky, Spectra::GEIGS_CHOLESKY> - geigs(&op, &Bop, 1, 15 < matrixDim ? 15 : matrixDim); + geigs(&op, &Bop, 1, ncv); // Initialize and compute geigs.init(); @@ -324,9 +327,12 @@ class EigenvaluesProblems, E Spectra::DenseSymMatProd op(B); Spectra::DenseCholesky Bop(-A); - // Construct generalized eigen solver object, requesting the largest three generalized eigenvalues + // Construct generalized eigen solver object, requesting the largest generalized eigenvalue + // an empirical value of ncv that gives a better performance + // TODO: tune this implementation by tuning the parameters like ncv + int ncv = std::min(std::max(10, matrixDim/20), matrixDim); Spectra::SymGEigsSolver, Spectra::DenseCholesky, Spectra::GEIGS_CHOLESKY> - geigs(&op, &Bop, 1, 15 < matrixDim ? 15 : matrixDim); + geigs(&op, &Bop, 1, ncv); // Initialize and compute geigs.init(); @@ -439,10 +445,39 @@ class EigenvaluesProblems, E /// \param[in] B: symmetric matrix /// \return The minimum positive eigenvalue and the corresponding eigenvector NT minPosLinearEigenvalue_EigenSymSolver(MT const & A, MT const & B, VT &eigvec) const { + +#if defined(SPECTRA_EIGENVALUES_SOLVER) + int matrixDim = A.rows(); + NT lambdaMinPositive; + + Spectra::DenseSymMatProd op(B); + Spectra::DenseCholesky Bop(A); + + //construct generalized eigen solver object, requesting the smallest eigenvalue + int ncv = std::min(std::max(10, matrixDim/20), matrixDim); + Spectra::SymGEigsSolver, Spectra::DenseCholesky, Spectra::GEIGS_CHOLESKY> + geigs(&op, &Bop, 1, ncv); + + //initialize and compute + geigs.init(); + int nconv = geigs.compute(); + + //retrieve results + VT evalues; + + if(geigs.info() == Spectra::SUCCESSFUL){ + evalues = geigs.eigenvalues(); + eigvec = geigs.eigenvectors().col(0); + } + + lambdaMinPositive = NT(1)/evalues(0); + +#elif NT lambdaMinPositive = NT(0); Eigen::GeneralizedSelfAdjointEigenSolver ges(B,A); lambdaMinPositive = 1/ges.eigenvalues().reverse()[0]; eigvec = ges.eigenvectors().reverse().col(0).reverse(); +#endif return lambdaMinPositive; } }; From ddcf97b96bf0884188eb114447adf4ab989e76f4 Mon Sep 17 00:00:00 2001 From: Apostolos Chalkis Date: Thu, 20 Jun 2024 11:54:16 -0600 Subject: [PATCH 25/35] Improve max volume ellipsoid computation (#309) * improve the implementation of maximum ellipsoid computation * minor fix in rounding unit-test * fix file copyrights * apply termination criterion after transformation in max ellipsoid rounding * resolve PR comments --------- Co-authored-by: Apostolos Chalkis --- .../preprocess/max_inscribed_ellipsoid.hpp | 78 +++++++++++++------ .../max_inscribed_ellipsoid_rounding.hpp | 39 +++++++--- test/new_rounding_test.cpp | 4 +- 3 files changed, 83 insertions(+), 38 deletions(-) diff --git a/include/preprocess/max_inscribed_ellipsoid.hpp b/include/preprocess/max_inscribed_ellipsoid.hpp index ebdb261e0..7a0c19a6f 100644 --- a/include/preprocess/max_inscribed_ellipsoid.hpp +++ b/include/preprocess/max_inscribed_ellipsoid.hpp @@ -1,7 +1,7 @@ // VolEsti (volume computation and sampling library) -// Copyright (c) 2012-2020 Vissarion Fisikopoulos -// Copyright (c) 2018-2020 Apostolos Chalkis +// Copyright (c) 2012-2024 Vissarion Fisikopoulos +// Copyright (c) 2018-2024 Apostolos Chalkis // Copyright (c) 2021 Vaibhav Thakkar //Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. @@ -15,6 +15,9 @@ #include #include +#include "Spectra/include/Spectra/SymEigsSolver.h" +#include "Spectra/include/Spectra/Util/SelectionRule.h" +#include "Spectra/include/Spectra/MatOp/DenseSymMatProd.h" /* @@ -31,14 +34,15 @@ tolerance parameters tol, reg Output: center of the ellipsoid y - matrix V = E_transpose * E + matrix E2^{-1} = E_transpose * E */ // using Custom_MT as to deal with both dense and sparse matrices, MT will be the type of result matrix +// TODO: Change the return data structure to std::tuple template std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT const& x0, - unsigned int const& maxiter, - NT const& tol, NT const& reg) + unsigned int const& maxiter, + NT const& tol, NT const& reg) { typedef Eigen::DiagonalMatrix Diagonal_MT; @@ -49,8 +53,8 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT last_r1 = std::numeric_limits::lowest(), last_r2 = std::numeric_limits::lowest(), prev_obj = std::numeric_limits::lowest(), - gap, rmu, res, objval, r1, r2 ,r3, rel, Rel, - astep, ax, ay, az, tau; + gap, rmu, res, objval, r1, r2 ,r3, astep, ax, + ay, az, tau, logdetE2; NT const reg_lim = std::pow(10.0, -10.0), tau0 = 0.75, minmu = std::pow(10.0, -8.0); @@ -74,9 +78,10 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT Y = y.asDiagonal(); - E2.noalias() = MT(A_trans * Y * A).inverse(); + E2.noalias() = MT(A_trans * Y * A); + Eigen::LLT llt(E2); - Q.noalias() = A * E2 * A_trans; + Q.noalias() = A * llt.solve(A_trans); h = Q.diagonal(); h = h.cwiseSqrt(); @@ -115,19 +120,38 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT res = std::max(r1, r2); res = std::max(res, r3); - objval = std::log(E2.determinant()) / 2.0; - - Eigen::SelfAdjointEigenSolver eigensolver(E2); // E2 is positive definite matrix - // computing eigenvalues of E2 - rel = eigensolver.eigenvalues().minCoeff(); - Rel = eigensolver.eigenvalues().maxCoeff(); + logdetE2 = llt.matrixL().toDenseMatrix().diagonal().array().log().sum(); + objval = logdetE2; //logdet of E2 is already divided by 2 if (i % 10 == 0) { + + NT rel, Rel; + + // computing eigenvalues of E2 + Spectra::DenseSymMatProd op(E2); + // The value of ncv is chosen empirically + Spectra::SymEigsSolver> eigs(&op, 2, std::min(std::max(10, n/5), n)); + eigs.init(); + int nconv = eigs.compute(); + if (eigs.info() == Spectra::COMPUTATION_INFO::SUCCESSFUL) { + Rel = 1.0 / eigs.eigenvalues().coeff(1); + rel = 1.0 / eigs.eigenvalues().coeff(0); + } else { + Eigen::SelfAdjointEigenSolver eigensolver(E2); // E2 is positive definite matrix + if (eigensolver.info() == Eigen::ComputationInfo::Success) { + Rel = 1.0 / eigensolver.eigenvalues().coeff(0); + rel = 1.0 / eigensolver.eigenvalues().template tail<1>().value(); + } else { + std::runtime_error("Computations failed."); + } + } if (std::abs((last_r1 - r1) / std::min(NT(std::abs(last_r1)), NT(std::abs(r1)))) < 0.01 && std::abs((last_r2 - r2) / std::min(NT(abs(last_r2)), NT(std::abs(r2)))) < 0.01 && Rel / rel > 100.0 && reg > reg_lim) { + converged = false; //Stopped making progress break; @@ -138,9 +162,10 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT // stopping criterion if ((res < tol * (1.0 + bnrm) && rmu <= minmu) || - (i > 4 && prev_obj != std::numeric_limits::lowest() && - ((std::abs(objval - prev_obj) <= tol * objval && std::abs(objval - prev_obj) <= tol * prev_obj) || - (prev_obj >= (1.0 - tol) * objval || objval <= (1.0 - tol) * prev_obj) ) ) ) { + (i > 1 && prev_obj != std::numeric_limits::lowest() && + (std::abs(objval - prev_obj) <= tol * std::min(std::abs(objval), std::abs(prev_obj)) || + std::abs(prev_obj - objval) <= tol) ) ) { + //converged x += x0; converged = true; @@ -151,7 +176,7 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT YQ.noalias() = Y * Q; G = YQ.cwiseProduct(YQ.transpose()); y2h = 2.0 * yh; - YA = Y * A; + YA.noalias() = Y * A; vec_iter1 = y2h.data(); vec_iter2 = z.data(); @@ -165,10 +190,9 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT G.diagonal() += y2h_z; h_z = h + z; + Eigen::PartialPivLU luG(G); + T.noalias() = luG.solve(h_z.asDiagonal()*YA); - for (int j = 0; j < n; ++j) { - T.col(j) = G.colPivHouseholderQr().solve( VT(YA.col(j).cwiseProduct(h_z)) ); - } ATP.noalias() = MT(y2h.asDiagonal()*T - YA).transpose(); vec_iter1 = R3.data(); @@ -184,11 +208,11 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT R23 = R2 - R3Dy; ATP_A.noalias() = ATP * A; ATP_A.diagonal() += ones_m * reg; - dx = ATP_A.colPivHouseholderQr().solve(R1 + ATP * R23); // predictor step + dx = ATP_A.lu().solve(R1 + ATP * R23); // predictor step // corrector and combined step & length Adx.noalias() = A * dx; - dyDy = G.colPivHouseholderQr().solve(y2h.cwiseProduct(Adx-R23)); + dyDy = luG.solve(y2h.cwiseProduct(Adx-R23)); dy = y.cwiseProduct(dyDy); dz = R3Dy - z.cwiseProduct(dyDy); @@ -228,9 +252,13 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT i++; } - return std::pair, bool>(std::pair(E2, x), converged); + if (!converged) { + x += x0; + } + return std::pair, bool>(std::pair(E2, x), converged); } #endif + diff --git a/include/preprocess/max_inscribed_ellipsoid_rounding.hpp b/include/preprocess/max_inscribed_ellipsoid_rounding.hpp index c13d53f70..da00a8867 100644 --- a/include/preprocess/max_inscribed_ellipsoid_rounding.hpp +++ b/include/preprocess/max_inscribed_ellipsoid_rounding.hpp @@ -22,14 +22,16 @@ template typename Point > std::tuple max_inscribed_ellipsoid_rounding(Polytope &P, - Point const& InnerPoint) + Point const& InnerPoint, + unsigned int const max_iterations = 5, + NT const max_eig_ratio = NT(6)) { std::pair, bool> iter_res; iter_res.second = false; VT x0 = InnerPoint.getCoefficients(); MT E, L; - unsigned int maxiter = 150, iter = 1, d = P.dimension(); + unsigned int maxiter = 500, iter = 1, d = P.dimension(); NT R = 100.0, r = 1.0, tol = std::pow(10, -6.0), reg = std::pow(10, -4.0), round_val = 1.0; @@ -44,18 +46,28 @@ std::tuple max_inscribed_ellipsoid_rounding(Polytope &P, E = (E + E.transpose()) / 2.0; E = E + MT::Identity(d, d)*std::pow(10, -8.0); //normalize E - Eigen::LLT lltOfA(E); // compute the Cholesky decomposition of E + Eigen::LLT lltOfA(E.llt().solve(MT::Identity(E.cols(), E.cols()))); // compute the Cholesky decomposition of E^{-1} L = lltOfA.matrixL(); - Eigen::SelfAdjointEigenSolver eigensolver(L); - r = eigensolver.eigenvalues().minCoeff(); - R = eigensolver.eigenvalues().maxCoeff(); - - // check the roundness of the polytope - if(((std::abs(R / r) <= 2.3 && iter_res.second) || iter >= 20) && iter>2){ - break; + // computing eigenvalues of E + Spectra::DenseSymMatProd op(E); + // The value of ncv is chosen empirically + Spectra::SymEigsSolver> eigs(&op, 2, std::min(std::max(10, int(d)/5), int(d))); + eigs.init(); + int nconv = eigs.compute(); + if (eigs.info() == Spectra::COMPUTATION_INFO::SUCCESSFUL) { + R = 1.0 / eigs.eigenvalues().coeff(1); + r = 1.0 / eigs.eigenvalues().coeff(0); + } else { + Eigen::SelfAdjointEigenSolver eigensolver(E); + if (eigensolver.info() == Eigen::ComputationInfo::Success) { + R = 1.0 / eigensolver.eigenvalues().coeff(0); + r = 1.0 / eigensolver.eigenvalues().template tail<1>().value(); + } else { + std::runtime_error("Computations failed."); + } } - // shift polytope and apply the linear transformation on P P.shift(iter_res.first.second); shift += T * iter_res.first.second; @@ -67,6 +79,11 @@ std::tuple max_inscribed_ellipsoid_rounding(Polytope &P, P.normalize(); x0 = VT::Zero(d); + // check the roundness of the polytope + if(((std::abs(R / r) <= max_eig_ratio && iter_res.second) || iter >= max_iterations)){ + break; + } + iter++; } diff --git a/test/new_rounding_test.cpp b/test/new_rounding_test.cpp index 87c63a32a..ab17ed939 100644 --- a/test/new_rounding_test.cpp +++ b/test/new_rounding_test.cpp @@ -26,6 +26,7 @@ #include "preprocess/svd_rounding.hpp" #include "generators/known_polytope_generators.h" +#include "generators/h_polytopes_generator.h" template NT factorial(NT n) @@ -108,7 +109,6 @@ void rounding_max_ellipsoid_test(Polytope &HP, typedef BoostRandomNumberGenerator RNGType; RNGType rng(d); - std::pair InnerBall = HP.ComputeInnerBall(); std::tuple res = max_inscribed_ellipsoid_rounding(HP, InnerBall.first); @@ -184,7 +184,7 @@ void call_test_max_ellipsoid() { std::cout << "\n--- Testing rounding of H-skinny_cube5" << std::endl; P = generate_skinny_cube(5); - rounding_max_ellipsoid_test(P, 0, 3070.64, 3188.25, 3140.6, 3200.0); + rounding_max_ellipsoid_test(P, 0, 3070.64, 3188.25, 3262.61, 3200.0); } From 723869e86f0e5953ad2afab4329e2538ef26c252 Mon Sep 17 00:00:00 2001 From: Apostolos Chalkis Date: Fri, 21 Jun 2024 11:47:00 -0600 Subject: [PATCH 26/35] Improve max ball computation and develop analytic center computation (#310) * improve the implementation of maximum ellipsoid computation * minor fix in rounding unit-test * fix file copyrights * fix max_ball bug and create new unit test * fix max_ball bug and create new unit test * apply termination criterion after transformation in max ellipsoid rounding * imrpove skinny poly generator's interface * improve stopping criterion and seed setting * complete unit tests implementations * implement Newton method to compute the analytic center * minor changes * resolve PR comments * complete analytic center computation * resolve conflicts * minor changes * minor changes * minor changes * improve new unit tests * resolve PR comments --------- Co-authored-by: Apostolos Chalkis --- include/convex_bodies/hpolytope.h | 8 +- include/generators/h_polytopes_generator.h | 82 ++++++++++- .../preprocess/analytic_center_linear_ineq.h | 134 ++++++++++++++++++ include/preprocess/max_inscribed_ball.hpp | 31 ++-- test/CMakeLists.txt | 11 ++ test/test_internal_points.cpp | 107 ++++++++++++++ 6 files changed, 352 insertions(+), 21 deletions(-) create mode 100644 include/preprocess/analytic_center_linear_ineq.h create mode 100644 test/test_internal_points.cpp diff --git a/include/convex_bodies/hpolytope.h b/include/convex_bodies/hpolytope.h index bee0fd369..9f4083bd7 100644 --- a/include/convex_bodies/hpolytope.h +++ b/include/convex_bodies/hpolytope.h @@ -116,13 +116,13 @@ class HPolytope { if (_inner_ball.second <= NT(0)) { - NT const tol = 0.00000001; - std::tuple inner_ball = max_inscribed_ball(A, b, 150, tol); + NT const tol = 1e-08; + std::tuple inner_ball = max_inscribed_ball(A, b, 5000, tol); // check if the solution is feasible - if (is_in(Point(std::get<0>(inner_ball))) == 0 || std::get<1>(inner_ball) < NT(0) || + if (is_in(Point(std::get<0>(inner_ball))) == 0 || std::get<1>(inner_ball) < tol/2.0 || std::isnan(std::get<1>(inner_ball)) || std::isinf(std::get<1>(inner_ball)) || - !std::get<2>(inner_ball) || is_inner_point_nan_inf(std::get<0>(inner_ball))) + is_inner_point_nan_inf(std::get<0>(inner_ball))) { _inner_ball.second = -1.0; } else diff --git a/include/generators/h_polytopes_generator.h b/include/generators/h_polytopes_generator.h index dae44bdca..e9638b62f 100644 --- a/include/generators/h_polytopes_generator.h +++ b/include/generators/h_polytopes_generator.h @@ -9,7 +9,11 @@ #define H_POLYTOPES_GEN_H #include +#include +#include +#include +#include "preprocess/max_inscribed_ellipsoid_rounding.hpp" #ifndef isnan using std::isnan; @@ -19,17 +23,17 @@ /// @tparam Polytope Type of returned polytope /// @tparam RNGType RNGType Type template -Polytope random_hpoly(unsigned int dim, unsigned int m, double seed = std::numeric_limits::signaling_NaN()) { +Polytope random_hpoly(unsigned int dim, unsigned int m, int seed = std::numeric_limits::signaling_NaN()) { typedef typename Polytope::MT MT; typedef typename Polytope::VT VT; typedef typename Polytope::NT NT; typedef typename Polytope::PointType Point; - unsigned rng_seed = std::chrono::system_clock::now().time_since_epoch().count(); + int rng_seed = std::chrono::system_clock::now().time_since_epoch().count(); RNGType rng(rng_seed); if (!isnan(seed)) { - unsigned rng_seed = seed; + int rng_seed = seed; rng.seed(rng_seed); } @@ -58,4 +62,76 @@ Polytope random_hpoly(unsigned int dim, unsigned int m, double seed = std::numer return Polytope(dim, A, b); } +/// This function generates a transformation that maps the unit ball to a skinny ellipsoid +/// with given ratio between the lengths of its maximum and minimum axis +template +MT get_skinny_transformation(const int d, NT const eig_ratio, int const seed) +{ + boost::normal_distribution<> gdist(0, 1); + RNGType rng(seed); + + MT W(d, d); + for (int i = 0; i < d; i++) { + for (int j = 0; j < d; j++) { + W(i, j) = gdist(rng); + } + } + + Eigen::HouseholderQR qr(W); + MT Q = qr.householderQ(); + + VT diag(d); + const NT eig_min = NT(1), eig_max = eig_ratio; + diag(0) = eig_min; + diag(d-1) = eig_max; + boost::random::uniform_real_distribution udist(NT(0), NT(1)); + NT rand; + for (int i = 1; i < d-1; i++) { + rand = udist(rng); + diag(i) = rand * eig_max + (NT(1)-rand) * eig_min; + } + std::sort(diag.begin(), diag.end()); + MT cov = Q * diag.asDiagonal() * Q.transpose(); + + return cov; +} + +/// This function generates a skinny random H-polytope of given dimension and number of hyperplanes $m$ +/// @tparam Polytope Type of returned polytope +/// @tparam NT Number type +/// @tparam RNGType RNGType Type +template +Polytope skinny_random_hpoly(unsigned int dim, unsigned int m, const bool pre_rounding = false, + const NT eig_ratio = NT(1000.0), int seed = std::numeric_limits::signaling_NaN()) { + + typedef typename Polytope::MT MT; + typedef typename Polytope::VT VT; + typedef typename Polytope::PointType Point; + + int rng_seed = std::chrono::system_clock::now().time_since_epoch().count(); + RNGType rng(rng_seed); + if (!isnan(seed)) { + int rng_seed = seed; + rng.seed(rng_seed); + } + + Polytope P = random_hpoly(dim, m, seed); + + // rounding the polytope before applying the skinny transformation + if (pre_rounding) { + Point x0(dim); + // run only one iteration + max_inscribed_ellipsoid_rounding(P, x0, 1); + } + + MT cov = get_skinny_transformation(dim, eig_ratio, seed); + Eigen::LLT lltOfA(cov); + MT L = lltOfA.matrixL(); + P.linear_transformIt(L.inverse()); + + return P; +} + + + #endif diff --git a/include/preprocess/analytic_center_linear_ineq.h b/include/preprocess/analytic_center_linear_ineq.h new file mode 100644 index 000000000..c7918635a --- /dev/null +++ b/include/preprocess/analytic_center_linear_ineq.h @@ -0,0 +1,134 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2024 Vissarion Fisikopoulos +// Copyright (c) 2024 Apostolos Chalkis +// Copyright (c) 2024 Elias Tsigaridas + +// Licensed under GNU LGPL.3, see LICENCE file + + +#ifndef ANALYTIC_CENTER_H +#define ANALYTIC_CENTER_H + +#include + +#include "max_inscribed_ball.hpp" + +template +NT get_max_step(VT const& Ad, VT const& b_Ax) +{ + const int m = Ad.size(); + NT max_element = std::numeric_limits::lowest(), max_element_temp; + for (int i = 0; i < m; i++) { + max_element_temp = Ad.coeff(i) / b_Ax.coeff(i); + if (max_element_temp > max_element) { + max_element = max_element_temp; + } + } + + return NT(1) / max_element; +} + +template +void get_hessian_grad_logbarrier(MT const& A, MT const& A_trans, VT const& b, + VT const& x, VT const& Ax, MT &H, VT &grad, VT &b_Ax) +{ + const int m = A.rows(); + VT s(m); + + b_Ax.noalias() = b - Ax; + NT *s_data = s.data(); + for (int i = 0; i < m; i++) + { + *s_data = NT(1) / b_Ax.coeff(i); + s_data++; + } + + VT s_sq = s.cwiseProduct(s); + // Gradient of the log-barrier function + grad.noalias() = A_trans * s; + // Hessian of the log-barrier function + H.noalias() = A_trans * s_sq.asDiagonal() * A; +} + +/* + This implementation computes the analytic center of a polytope given + as a set of linear inequalities P = {x | Ax <= b}. The analytic center + is the minimizer of the log barrier function i.e., the optimal solution + of the following optimization problem (Convex Optimization, Boyd and Vandenberghe, Section 8.5.3), + + \min -\sum \log(b_i - a_i^Tx), where a_i is the i-th row of A. + + The function solves the problem by using the Newton method. + + Input: (i) Matrix A, vector b such that the polytope P = {x | Ax<=b} + (ii) The number of maximum iterations, max_iters + (iii) Tolerance parameter grad_err_tol to bound the L2-norm of the gradient + (iv) Tolerance parameter rel_pos_err_tol to check the relative progress in each iteration + + Output: (i) The analytic center of the polytope + (ii) A boolean variable that declares convergence +*/ +template +std::tuple analytic_center_linear_ineq(MT const& A, VT const& b, + unsigned int const max_iters = 500, + NT const grad_err_tol = 1e-08, + NT const rel_pos_err_tol = 1e-12) +{ + VT x; + bool feasibility_only = true, converged; + // Compute a feasible point + std::tie(x, std::ignore, converged) = max_inscribed_ball(A, b, max_iters, 1e-08, feasibility_only); + VT Ax = A * x; + if (!converged || (Ax.array() > b.array()).any()) + { + std::runtime_error("The computation of the analytic center failed."); + } + // Initialization + const int n = A.cols(), m = A.rows(); + MT H(n, n), A_trans = A.transpose(); + VT grad(n), d(n), Ad(m), b_Ax(m), step_d(n), x_prev; + NT grad_err, rel_pos_err, rel_pos_err_temp, step; + unsigned int iter = 0; + converged = false; + const NT tol_bnd = NT(0.01); + + get_hessian_grad_logbarrier(A, A_trans, b, x, Ax, H, grad, b_Ax); + + do { + iter++; + // Compute the direction + d.noalias() = - H.llt().solve(grad); + Ad.noalias() = A * d; + // Compute the step length + step = std::min((NT(1) - tol_bnd) * get_max_step(Ad, b_Ax), NT(1)); + step_d.noalias() = step*d; + x_prev = x; + x += step_d; + Ax.noalias() += step*Ad; + + // Compute the max_i\{ |step*d_i| ./ |x_i| \} + rel_pos_err = std::numeric_limits::lowest(); + for (int i = 0; i < n; i++) + { + rel_pos_err_temp = std::abs(step_d.coeff(i) / x_prev.coeff(i)); + if (rel_pos_err_temp > rel_pos_err) + { + rel_pos_err = rel_pos_err_temp; + } + } + + get_hessian_grad_logbarrier(A, A_trans, b, x, Ax, H, grad, b_Ax); + grad_err = grad.norm(); + + if (iter >= max_iters || grad_err <= grad_err_tol || rel_pos_err <= rel_pos_err_tol) + { + converged = true; + break; + } + } while (true); + + return std::make_tuple(x, converged); +} + +#endif diff --git a/include/preprocess/max_inscribed_ball.hpp b/include/preprocess/max_inscribed_ball.hpp index 6fe70e9a0..672be24e4 100644 --- a/include/preprocess/max_inscribed_ball.hpp +++ b/include/preprocess/max_inscribed_ball.hpp @@ -27,7 +27,7 @@ */ template -void calcstep(MT const& A, MT const& A_trans, MT const& R, VT &s, +void calcstep(MT const& A, MT const& A_trans, Eigen::LLT const& lltOfB, VT &s, VT &y, VT &r1, VT const& r2, NT const& r3, VT &r4, VT &dx, VT &ds, NT &dt, VT &dy, VT &tmp, VT &rhs) { @@ -41,7 +41,8 @@ void calcstep(MT const& A, MT const& A_trans, MT const& R, VT &s, rhs.block(0,0,n,1).noalias() = r2 + A_trans * tmp; rhs(n) = r3 + tmp.sum(); - VT dxdt = R.colPivHouseholderQr().solve(R.transpose().colPivHouseholderQr().solve(rhs)); + + VT dxdt = lltOfB.solve(rhs); dx = dxdt.block(0,0,n,1); dt = dxdt(n); @@ -57,10 +58,12 @@ void calcstep(MT const& A, MT const& A_trans, MT const& R, VT &s, template -std::tuple max_inscribed_ball(MT const& A, VT const& b, unsigned int maxiter, NT tol) +std::tuple max_inscribed_ball(MT const& A, VT const& b, + unsigned int maxiter, NT tol, + const bool feasibility_only = false) { int m = A.rows(), n = A.cols(); - bool converge; + bool converge = false; NT bnrm = b.norm(); VT o_m = VT::Zero(m), o_n = VT::Zero(n), e_m = VT::Ones(m); @@ -81,7 +84,7 @@ std::tuple max_inscribed_ball(MT const& A, VT const& b, unsigned NT const tau0 = 0.995, power_num = 5.0 * std::pow(10.0, 15.0); NT *vec_iter1, *vec_iter2, *vec_iter3, *vec_iter4; - MT B(n + 1, n + 1), AtD(n, m), R(n + 1, n + 1), + MT B(n + 1, n + 1), AtD(n, m), eEye = std::pow(10.0, -14.0) * MT::Identity(n + 1, n + 1), A_trans = A.transpose(); @@ -105,16 +108,17 @@ std::tuple max_inscribed_ball(MT const& A, VT const& b, unsigned total_err = std::max(total_err, rgap); // progress output & check stopping - if (total_err < tol || ( t > 0 && ( (std::abs(t - t_prev) <= tol * t && std::abs(t - t_prev) <= tol * t_prev) - || (t_prev >= (1.0 - tol) * t && i > 0) - || (t <= (1.0 - tol) * t_prev && i > 0) ) ) ) + if ( (total_err < tol && t > 0) || + ( t > 0 && ( (std::abs(t - t_prev) <= tol * std::min(std::abs(t), std::abs(t_prev)) || + std::abs(t - t_prev) <= tol) && i > 10) ) || + (feasibility_only && t > tol/2.0 && i > 0) ) { //converged converge = true; break; } - if (dt > 1000.0 * bnrm || t > 1000000.0 * bnrm) + if ((dt > 10000.0 * bnrm || t > 10000000.0 * bnrm) && i > 20) { //unbounded converge = false; @@ -127,11 +131,11 @@ std::tuple max_inscribed_ball(MT const& A, VT const& b, unsigned vec_iter2 = y.data(); for (int j = 0; j < m; ++j) { *vec_iter1 = std::min(power_num, (*vec_iter2) / (*vec_iter3)); - AtD.col(j).noalias() = A_trans.col(j) * (*vec_iter1); vec_iter1++; vec_iter3++; vec_iter2++; } + AtD.noalias() = A_trans*d.asDiagonal(); AtDe.noalias() = AtD * e_m; B.block(0, 0, n, n).noalias() = AtD * A; @@ -141,11 +145,10 @@ std::tuple max_inscribed_ball(MT const& A, VT const& b, unsigned B.noalias() += eEye; // Cholesky decomposition - Eigen::LLT lltOfB(B); - R = lltOfB.matrixL().transpose(); + Eigen::LLT lltOfB(B); // predictor step & length - calcstep(A, A_trans, R, s, y, r1, r2, r3, r4, dx, ds, dt, dy, tmp, rhs); + calcstep(A, A_trans, lltOfB, s, y, r1, r2, r3, r4, dx, ds, dt, dy, tmp, rhs); alphap = -1.0; alphad = -1.0; @@ -172,7 +175,7 @@ std::tuple max_inscribed_ball(MT const& A, VT const& b, unsigned // corrector and combined step & length mu_ds_dy.noalias() = e_m * mu - ds.cwiseProduct(dy); - calcstep(A, A_trans, R, s, y, o_m, o_n, 0.0, mu_ds_dy, dxc, dsc, dtc, dyc, tmp, rhs); + calcstep(A, A_trans, lltOfB, s, y, o_m, o_n, 0.0, mu_ds_dy, dxc, dsc, dtc, dyc, tmp, rhs); dx += dxc; ds += dsc; diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index a3f8f03d1..e17848fc6 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -297,6 +297,8 @@ add_test(NAME test_round_max_ellipsoid add_test(NAME test_round_svd COMMAND new_rounding_test -tc=round_svd) + + add_executable (logconcave_sampling_test logconcave_sampling_test.cpp $) add_test(NAME logconcave_sampling_test_hmc COMMAND logconcave_sampling_test -tc=hmc) @@ -348,6 +350,14 @@ add_test(NAME test_new_rdhr_uniform_MT COMMAND matrix_sampling_test -tc=new_rdhr add_test(NAME test_new_billiard_uniform_MT COMMAND matrix_sampling_test -tc=new_billiard_uniform_MT) add_test(NAME test_new_accelerated_billiard_uniform_MT COMMAND matrix_sampling_test -tc=new_accelerated_billiard_uniform_MT) +add_executable (test_internal_points test_internal_points.cpp $) +add_test(NAME test_max_ball + COMMAND test_internal_points -tc=test_max_ball) +add_test(NAME test_feasibility_point + COMMAND test_internal_points -tc=test_feasibility_point) +add_test(NAME test_analytic_center + COMMAND test_internal_points -tc=test_analytic_center) + set(ADDITIONAL_FLAGS "-march=native -DSIMD_LEN=0 -DTIME_KEEPING") #set_target_properties(benchmarks_crhmc @@ -388,3 +398,4 @@ TARGET_LINK_LIBRARIES(logconcave_sampling_test lp_solve ${IFOPT} ${IFOPT_IPOPT} TARGET_LINK_LIBRARIES(crhmc_sampling_test lp_solve ${IFOPT} ${IFOPT_IPOPT} ${PTHREAD} ${GMP} ${MPSOLVE} ${FFTW3} ${MKL_LINK} QD_LIB coverage_config) TARGET_LINK_LIBRARIES(order_polytope lp_solve coverage_config) TARGET_LINK_LIBRARIES(matrix_sampling_test lp_solve ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(test_internal_points lp_solve ${MKL_LINK} coverage_config) diff --git a/test/test_internal_points.cpp b/test/test_internal_points.cpp new file mode 100644 index 000000000..fa44e9baf --- /dev/null +++ b/test/test_internal_points.cpp @@ -0,0 +1,107 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2024 Vissarion Fisikopoulos +// Copyright (c) 2024 Apostolos Chalkis +// Copyright (c) 2024 Elias Tsigaridas + +// Licensed under GNU LGPL.3, see LICENCE file + +#include "doctest.h" +#include +#include + +#include + +#include "misc/misc.h" +#include "cartesian_geom/cartesian_kernel.h" +#include "convex_bodies/hpolytope.h" + +#include "preprocess/max_inscribed_ball.hpp" +#include "preprocess/analytic_center_linear_ineq.h" + +#include "generators/known_polytope_generators.h" +#include "generators/h_polytopes_generator.h" + +template +void call_test_max_ball() { + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef HPolytope Hpolytope; + typedef boost::mt19937 PolyRNGType; + Hpolytope P; + + std::cout << "\n--- Testing Chebychev ball for skinny H-polytope" << std::endl; + bool pre_rounding = true; // round random polytope before applying the skinny transformation + NT max_min_eig_ratio = NT(2000); + P = skinny_random_hpoly(4, 180, pre_rounding, max_min_eig_ratio, 127); + P.normalize(); + std::pair InnerBall = P.ComputeInnerBall(); + + NT tol = 1e-08; + unsigned int maxiter = 500; + auto [center, radius, converged] = max_inscribed_ball(P.get_mat(), P.get_vec(), maxiter, tol); + + CHECK(P.is_in(Point(center)) == -1); + CHECK(std::abs(radius - InnerBall.second) <= 1e-03); + CHECK(converged); +} + +template +void call_test_max_ball_feasibility() { + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef HPolytope Hpolytope; + typedef boost::mt19937 PolyRNGType; + Hpolytope P; + + std::cout << "\n--- Testing feasibility point for skinny H-polytope" << std::endl; + bool pre_rounding = true; // round random polytope before applying the skinny transformation + NT max_min_eig_ratio = NT(2000); + P = skinny_random_hpoly(50, 500, pre_rounding, max_min_eig_ratio, 127); + P.normalize(); + + bool feasibility_only = true; // compute only a feasible point + NT tol = 1e-08; + unsigned int maxiter = 500; + auto [center, radius, converged] = max_inscribed_ball(P.get_mat(), P.get_vec(), maxiter, tol, feasibility_only); + + CHECK(P.is_in(Point(center)) == -1); + CHECK(converged); +} + +template +void call_test_analytic_center() { + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef HPolytope Hpolytope; + typedef typename Hpolytope::MT MT; + typedef typename Hpolytope::VT VT; + typedef boost::mt19937 PolyRNGType; + Hpolytope P; + + std::cout << "\n--- Testing analytic center for skinny H-polytope" << std::endl; + bool pre_rounding = true; // round random polytope before applying the skinny transformation + NT max_min_eig_ratio = NT(100); + P = skinny_random_hpoly(3, 15, pre_rounding, max_min_eig_ratio, 127); + P.normalize(); + + auto [analytic_center, converged] = analytic_center_linear_ineq(P.get_mat(), P.get_vec()); + + CHECK(P.is_in(Point(analytic_center)) == -1); + CHECK(converged); + CHECK(std::abs(analytic_center(0) + 4.75912) < 1e-04); + CHECK(std::abs(analytic_center(1) + 4.28762) < 1e-04); + CHECK(std::abs(analytic_center(2) - 7.54156) < 1e-04); +} + +TEST_CASE("test_max_ball") { + call_test_max_ball(); +} + +TEST_CASE("test_feasibility_point") { + call_test_max_ball_feasibility(); +} + +TEST_CASE("test_analytic_center") { + call_test_analytic_center(); +} From d076bf0a67a24550408cae13ed79f8e68dccba02 Mon Sep 17 00:00:00 2001 From: Vladimir Necula <151810681+vgnecula@users.noreply.github.com> Date: Tue, 25 Jun 2024 05:17:14 -0400 Subject: [PATCH 27/35] Position Nudging after Position Update (#308) * Position Nudging after Position Update * Complexity improvements and Polytope Normalization * HPolytope Normalization Flag * Polytope normalization within Walk Constructor * Alias HPolytope Normalization for Nudging inside Gaussian HMC * Polytope Normalization in ComputeInner Ball Fixed * Polytope Normalization Style change * Nudge in function within Gaussian HMC, and restore Hpoly file * More efficient Nudge in Process --- include/convex_bodies/hpolytope.h | 2 +- ...ian_hamiltonian_monte_carlo_exact_walk.hpp | 55 +++++++++++++++++-- 2 files changed, 51 insertions(+), 6 deletions(-) diff --git a/include/convex_bodies/hpolytope.h b/include/convex_bodies/hpolytope.h index 9f4083bd7..d0e894faa 100644 --- a/include/convex_bodies/hpolytope.h +++ b/include/convex_bodies/hpolytope.h @@ -927,4 +927,4 @@ class HPolytope { } }; -#endif +#endif \ No newline at end of file diff --git a/include/random_walks/gaussian_hamiltonian_monte_carlo_exact_walk.hpp b/include/random_walks/gaussian_hamiltonian_monte_carlo_exact_walk.hpp index bda33630c..b0140a0a1 100644 --- a/include/random_walks/gaussian_hamiltonian_monte_carlo_exact_walk.hpp +++ b/include/random_walks/gaussian_hamiltonian_monte_carlo_exact_walk.hpp @@ -53,6 +53,7 @@ struct Walk typedef typename Polytope::PointType Point; typedef typename Point::FT NT; typedef typename Polytope::VT VT; + typedef typename Polytope::MT MT; template Walk(GenericPolytope &P, Point const& p, NT const& a_i, RandomNumberGenerator &rng) @@ -80,7 +81,7 @@ struct Walk < typename GenericPolytope > - inline void apply(GenericPolytope& P, + inline void apply(GenericPolytope const& P, Point& p, NT const& a_i, unsigned int const& walk_length, @@ -89,6 +90,9 @@ struct Walk unsigned int n = P.dimension(); NT T; + GenericPolytope P_normalized = P; + P_normalized.normalize(); + for (auto j=0u; j - inline void get_starting_point(GenericPolytope& P, + inline void get_starting_point(GenericPolytope const& P, Point const& center, Point &q, unsigned int const& walk_length, @@ -141,7 +146,7 @@ struct Walk < typename GenericPolytope > - inline void parameters_burnin(GenericPolytope& P, + inline void parameters_burnin(GenericPolytope const& P, Point const& center, unsigned int const& num_points, unsigned int const& walk_length, @@ -191,7 +196,7 @@ private : < typename GenericPolytope > - inline void initialize(GenericPolytope& P, + inline void initialize(GenericPolytope const& P, Point const& p, NT const& a_i, RandomNumberGenerator &rng) @@ -204,6 +209,9 @@ private : NT T = rng.sample_urdist() * _Len; int it = 0; + GenericPolytope P_normalized = P; + P_normalized.normalize(); + while (it <= _rho) { auto pbpair @@ -218,12 +226,50 @@ private : } _lambda_prev = pbpair.first; update_position(_p, _v, _lambda_prev, _omega); + nudge_in(P_normalized, _p); T -= _lambda_prev; P.compute_reflection(_v, _p, pbpair.second); it++; } } + template + < + typename GenericPolytope + > + inline void nudge_in(GenericPolytope& P, Point& p, NT tol=NT(0)) + { + MT A = P.get_mat(); + VT b = P.get_vec(); + int m = A.rows(); + + VT b_Ax = b - A * p.getCoefficients(); + const NT* b_Ax_data = b_Ax.data(); + + NT dist; + + for (int i = 0; i < m; i++) { + + dist = *b_Ax_data; + + if (dist < NT(-tol)){ + //Nudging correction + NT eps = -1e-7; + + NT eps_1 = -dist; + //A.row is already normalized, no need to do it again + VT A_i = A.row(i); + NT eps_2 = eps_1 + eps; + + //Nudge the point inside with respect to the normal its vector + Point shift(A_i); + shift.operator*=(eps_2); + p.operator+=(shift); + } + b_Ax_data++; + } + } + inline void update_position(Point &p, Point &v, NT const& T, NT const& omega) { NT next_p, next_v; @@ -274,4 +320,3 @@ private : #endif // RANDOM_WALKS_GAUSSIAN_HMC_WALK_HPP - From 5bc28d68d73fe8a6d3ed1111f7a10c976f600de6 Mon Sep 17 00:00:00 2001 From: Apostolos Chalkis Date: Thu, 27 Jun 2024 02:17:47 -0600 Subject: [PATCH 28/35] Generalize the rounding loop and support sparse computations in preprocessing routines (#312) * generalize rounding loop * support sparse cholesky operator * complete sparse support in max_inscribed_ball * complete sparse support in preprocesing * add sparse tests * change main rounding function name * improve explaining comments * resolve PR comments * changing the dates in copyrights * use if constexpr instead of SNIFAE * update the examples to cpp17 * update to cpp17 order polytope example * fix templating in mat_computational_operators * fix templating errors and change header file to mat_computational_operators --------- Co-authored-by: Apostolos Chalkis --- examples/EnvelopeProblemSOS/CMakeLists.txt | 4 +- .../CMakeLists.txt | 1 + .../volesti_lecount.cpp | 2 +- examples/crhmc_prepare/CMakeLists.txt | 1 + examples/crhmc_sampling/CMakeLists.txt | 2 +- examples/ellipsoid-sampling/CMakeLists.txt | 2 +- examples/hpolytope-volume/CMakeLists.txt | 1 + examples/logconcave/CMakeLists.txt | 2 +- examples/mmcs_method/CMakeLists.txt | 2 +- examples/multithread_sampling/CMakeLists.txt | 2 +- .../optimization_spectrahedra/CMakeLists.txt | 1 + examples/order-polytope-basics/CMakeLists.txt | 2 +- .../CMakeLists.txt | 2 +- .../sampler.cpp | 17 +- examples/volume_spectrahedron/CMakeLists.txt | 2 +- examples/vpolytope-volume/CMakeLists.txt | 1 + include/convex_bodies/orderpolytope.h | 6 + include/generators/h_polytopes_generator.h | 4 +- .../preprocess/analytic_center_linear_ineq.h | 62 ++-- .../estimate_L_smooth_parameter.hpp | 4 +- include/preprocess/feasible_point.hpp | 34 ++ .../inscribed_ellipsoid_rounding.hpp | 134 ++++++++ .../preprocess/mat_computational_operators.h | 316 ++++++++++++++++++ include/preprocess/max_inscribed_ball.hpp | 48 ++- .../preprocess/max_inscribed_ellipsoid.hpp | 58 ++-- .../max_inscribed_ellipsoid_rounding.hpp | 94 ------ test/CMakeLists.txt | 18 +- test/max_ellipsoid_rounding_test.cpp | 4 +- ...ew_rounding_test.cpp => rounding_test.cpp} | 117 ++++++- test/test_internal_points.cpp | 63 +++- 30 files changed, 786 insertions(+), 220 deletions(-) create mode 100644 include/preprocess/feasible_point.hpp create mode 100644 include/preprocess/inscribed_ellipsoid_rounding.hpp create mode 100644 include/preprocess/mat_computational_operators.h delete mode 100644 include/preprocess/max_inscribed_ellipsoid_rounding.hpp rename test/{new_rounding_test.cpp => rounding_test.cpp} (61%) diff --git a/examples/EnvelopeProblemSOS/CMakeLists.txt b/examples/EnvelopeProblemSOS/CMakeLists.txt index e8f5b1a61..6d4e2a2a4 100644 --- a/examples/EnvelopeProblemSOS/CMakeLists.txt +++ b/examples/EnvelopeProblemSOS/CMakeLists.txt @@ -1,7 +1,7 @@ cmake_minimum_required(VERSION 3.15) project(EnvelopeProblem) -set(CMAKE_CXX_STANDARD 14) +set(CMAKE_CXX_STANDARD 17) set(CMAKE_CXX_FLAGS_DEBUG_CUSTOM "-O0 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -DIPM_USE_DOUBLE -DIPM_DOUBLE=double") set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} ${CMAKE_CXX_FLAGS_DEBUG_CUSTOM}") @@ -83,4 +83,4 @@ else () target_link_directories(EnvelopeProblem PRIVATE ${OPENMP_LIBRARIES}) endif () target_link_libraries(EnvelopeProblem Python2::Python Python2::NumPy) -endif () \ No newline at end of file +endif () diff --git a/examples/count-linear-extensions-using-hpolytope/CMakeLists.txt b/examples/count-linear-extensions-using-hpolytope/CMakeLists.txt index f3e66b634..49f295303 100644 --- a/examples/count-linear-extensions-using-hpolytope/CMakeLists.txt +++ b/examples/count-linear-extensions-using-hpolytope/CMakeLists.txt @@ -98,6 +98,7 @@ else () add_compile_definitions("EIGEN_NO_DEBUG") endif () + add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") diff --git a/examples/count-linear-extensions-using-hpolytope/volesti_lecount.cpp b/examples/count-linear-extensions-using-hpolytope/volesti_lecount.cpp index 9b0e99822..eb7133155 100644 --- a/examples/count-linear-extensions-using-hpolytope/volesti_lecount.cpp +++ b/examples/count-linear-extensions-using-hpolytope/volesti_lecount.cpp @@ -10,7 +10,7 @@ #include "volume_cooling_gaussians.hpp" #include "volume_cooling_balls.hpp" -#include "preprocess/max_inscribed_ellipsoid_rounding.hpp" +#include "preprocess/inscribed_ellipsoid_rounding.hpp" #include "preprocess/min_sampling_covering_ellipsoid_rounding.hpp" #include "preprocess/svd_rounding.hpp" diff --git a/examples/crhmc_prepare/CMakeLists.txt b/examples/crhmc_prepare/CMakeLists.txt index 3575c4a46..9d99477c2 100644 --- a/examples/crhmc_prepare/CMakeLists.txt +++ b/examples/crhmc_prepare/CMakeLists.txt @@ -105,6 +105,7 @@ endif () #add_definitions(${CMAKE_CXX_FLAGS} "-g") # enable debuger +add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") # enable C++17 standard set(ADDITIONAL_FLAGS "-march=native -DSIMD_LEN=0 -DTIME_KEEPING") add_definitions(${CMAKE_CXX_FLAGS} "-O3 " ${ADDITIONAL_FLAGS}) # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") diff --git a/examples/crhmc_sampling/CMakeLists.txt b/examples/crhmc_sampling/CMakeLists.txt index 91fe46a7f..e3dddb905 100644 --- a/examples/crhmc_sampling/CMakeLists.txt +++ b/examples/crhmc_sampling/CMakeLists.txt @@ -113,7 +113,7 @@ else () endif () - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard + add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") # enable C++17 standard set(ADDITIONAL_FLAGS "-march=native -DSIMD_LEN=0 -DTIME_KEEPING") add_definitions(${CMAKE_CXX_FLAGS} "-O3 -DTIME_KEEPING" ${ADDITIONAL_FLAGS}) # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") diff --git a/examples/ellipsoid-sampling/CMakeLists.txt b/examples/ellipsoid-sampling/CMakeLists.txt index b275ed9b9..d16704b17 100644 --- a/examples/ellipsoid-sampling/CMakeLists.txt +++ b/examples/ellipsoid-sampling/CMakeLists.txt @@ -99,7 +99,7 @@ else () endif () - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard + add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") # enable C++11 standard add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") diff --git a/examples/hpolytope-volume/CMakeLists.txt b/examples/hpolytope-volume/CMakeLists.txt index eff88889a..ad9bb7e39 100644 --- a/examples/hpolytope-volume/CMakeLists.txt +++ b/examples/hpolytope-volume/CMakeLists.txt @@ -88,6 +88,7 @@ include_directories (BEFORE ../../include/nlp_oracles) include_directories (BEFORE ../../include/misc) include_directories (BEFORE ../../include/optimization) +add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-ldl") diff --git a/examples/logconcave/CMakeLists.txt b/examples/logconcave/CMakeLists.txt index 33725d0c9..cc62d286c 100644 --- a/examples/logconcave/CMakeLists.txt +++ b/examples/logconcave/CMakeLists.txt @@ -123,7 +123,7 @@ else () endif () - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard + add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") # enable C++11 standard add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") diff --git a/examples/mmcs_method/CMakeLists.txt b/examples/mmcs_method/CMakeLists.txt index 01b8a040c..5ecd129f2 100644 --- a/examples/mmcs_method/CMakeLists.txt +++ b/examples/mmcs_method/CMakeLists.txt @@ -97,7 +97,7 @@ else () endif () - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard + add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") # enable C++17 standard add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") diff --git a/examples/multithread_sampling/CMakeLists.txt b/examples/multithread_sampling/CMakeLists.txt index ded4b4dbb..81ad78285 100644 --- a/examples/multithread_sampling/CMakeLists.txt +++ b/examples/multithread_sampling/CMakeLists.txt @@ -97,7 +97,7 @@ else () endif () - #add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard + add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") # enable C++17 standard add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") diff --git a/examples/optimization_spectrahedra/CMakeLists.txt b/examples/optimization_spectrahedra/CMakeLists.txt index a35aee4c7..7c941640c 100644 --- a/examples/optimization_spectrahedra/CMakeLists.txt +++ b/examples/optimization_spectrahedra/CMakeLists.txt @@ -10,6 +10,7 @@ if(COMMAND cmake_policy) cmake_policy(SET CMP0003 NEW) endif(COMMAND cmake_policy) +add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") add_executable (read_write_sdpa_file read_write_sdpa_file.cpp) diff --git a/examples/order-polytope-basics/CMakeLists.txt b/examples/order-polytope-basics/CMakeLists.txt index b45ef374c..70026f61b 100644 --- a/examples/order-polytope-basics/CMakeLists.txt +++ b/examples/order-polytope-basics/CMakeLists.txt @@ -99,7 +99,7 @@ else () endif () - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard + add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") # enable C++17 standard add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") diff --git a/examples/sampling-hpolytope-with-billiard-walks/CMakeLists.txt b/examples/sampling-hpolytope-with-billiard-walks/CMakeLists.txt index c10fb041c..5c47fe0ad 100644 --- a/examples/sampling-hpolytope-with-billiard-walks/CMakeLists.txt +++ b/examples/sampling-hpolytope-with-billiard-walks/CMakeLists.txt @@ -102,7 +102,7 @@ else () endif () - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard + add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") # enable C++17 standard add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") diff --git a/examples/sampling-hpolytope-with-billiard-walks/sampler.cpp b/examples/sampling-hpolytope-with-billiard-walks/sampler.cpp index 22dd6d9e5..803d38dc3 100644 --- a/examples/sampling-hpolytope-with-billiard-walks/sampler.cpp +++ b/examples/sampling-hpolytope-with-billiard-walks/sampler.cpp @@ -73,17 +73,16 @@ void sample_using_gaussian_billiard_walk(HPOLYTOPE& HP, RNGType& rng, unsigned i unsigned int max_iter = 150; NT tol = std::pow(10, -6.0), reg = std::pow(10, -4.0); VT x0 = q.getCoefficients(); - std::pair, bool> inscribed_ellipsoid_res = max_inscribed_ellipsoid(HP.get_mat(), - HP.get_vec(), - x0, - max_iter, - tol, - reg); - if (!inscribed_ellipsoid_res.second) // not converged + MT E; + VT center; + bool converged; + std::tie(E, center, converged) = max_inscribed_ellipsoid(HP.get_mat(), + HP.get_vec(), x0, max_iter, tol, reg); + + if (!converged) // not converged throw std::runtime_error("max_inscribed_ellipsoid not converged"); - MT A_ell = inscribed_ellipsoid_res.first.first.inverse(); - EllipsoidType inscribed_ellipsoid(A_ell); + EllipsoidType inscribed_ellipsoid(E); // -------------------------------------------------------------------- Generator::apply(HP, q, inscribed_ellipsoid, num_points, walk_len, diff --git a/examples/volume_spectrahedron/CMakeLists.txt b/examples/volume_spectrahedron/CMakeLists.txt index 424c457b3..57ecd27e0 100644 --- a/examples/volume_spectrahedron/CMakeLists.txt +++ b/examples/volume_spectrahedron/CMakeLists.txt @@ -98,7 +98,7 @@ else () endif () - add_definitions(${CMAKE_CXX_FLAGS} "-std=c++11") # enable C++11 standard + add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") # enable C++17 standard add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") diff --git a/examples/vpolytope-volume/CMakeLists.txt b/examples/vpolytope-volume/CMakeLists.txt index e1a0c2576..2f7020fb1 100644 --- a/examples/vpolytope-volume/CMakeLists.txt +++ b/examples/vpolytope-volume/CMakeLists.txt @@ -91,6 +91,7 @@ else () add_compile_definitions("EIGEN_NO_DEBUG") endif () +add_definitions(${CMAKE_CXX_FLAGS} "-std=c++17") add_definitions(${CMAKE_CXX_FLAGS} "-O3") # optimization of the compiler #add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lgsl") add_definitions(${CXX_COVERAGE_COMPILE_FLAGS} "-lm") diff --git a/include/convex_bodies/orderpolytope.h b/include/convex_bodies/orderpolytope.h index 2df5c6619..79f41e161 100644 --- a/include/convex_bodies/orderpolytope.h +++ b/include/convex_bodies/orderpolytope.h @@ -638,6 +638,12 @@ class OrderPolytope { return false; } + // Apply linear transformation, of square matrix T^{-1}, in H-polytope P:= Ax<=b + // This is most of the times for testing reasons because it might destroy the sparsity + void linear_transformIt(MT const& T) + { + _A = _A * T; + } // shift polytope by a point c void shift(VT const& c) diff --git a/include/generators/h_polytopes_generator.h b/include/generators/h_polytopes_generator.h index e9638b62f..0c9293baa 100644 --- a/include/generators/h_polytopes_generator.h +++ b/include/generators/h_polytopes_generator.h @@ -13,7 +13,7 @@ #include #include -#include "preprocess/max_inscribed_ellipsoid_rounding.hpp" +#include "preprocess/inscribed_ellipsoid_rounding.hpp" #ifndef isnan using std::isnan; @@ -121,7 +121,7 @@ Polytope skinny_random_hpoly(unsigned int dim, unsigned int m, const bool pre_ro if (pre_rounding) { Point x0(dim); // run only one iteration - max_inscribed_ellipsoid_rounding(P, x0, 1); + inscribed_ellipsoid_rounding(P, x0, 1); } MT cov = get_skinny_transformation(dim, eig_ratio, seed); diff --git a/include/preprocess/analytic_center_linear_ineq.h b/include/preprocess/analytic_center_linear_ineq.h index c7918635a..80d731f6c 100644 --- a/include/preprocess/analytic_center_linear_ineq.h +++ b/include/preprocess/analytic_center_linear_ineq.h @@ -12,7 +12,9 @@ #include -#include "max_inscribed_ball.hpp" +#include "preprocess/max_inscribed_ball.hpp" +#include "preprocess/feasible_point.hpp" +#include "preprocess/mat_computational_operators.h" template NT get_max_step(VT const& Ad, VT const& b_Ax) @@ -33,22 +35,13 @@ template void get_hessian_grad_logbarrier(MT const& A, MT const& A_trans, VT const& b, VT const& x, VT const& Ax, MT &H, VT &grad, VT &b_Ax) { - const int m = A.rows(); - VT s(m); - b_Ax.noalias() = b - Ax; - NT *s_data = s.data(); - for (int i = 0; i < m; i++) - { - *s_data = NT(1) / b_Ax.coeff(i); - s_data++; - } - + VT s = b_Ax.cwiseInverse(); VT s_sq = s.cwiseProduct(s); // Gradient of the log-barrier function grad.noalias() = A_trans * s; // Hessian of the log-barrier function - H.noalias() = A_trans * s_sq.asDiagonal() * A; + update_Atrans_Diag_A(H, A_trans, A, s_sq.asDiagonal()); } /* @@ -66,39 +59,36 @@ void get_hessian_grad_logbarrier(MT const& A, MT const& A_trans, VT const& b, (iii) Tolerance parameter grad_err_tol to bound the L2-norm of the gradient (iv) Tolerance parameter rel_pos_err_tol to check the relative progress in each iteration - Output: (i) The analytic center of the polytope - (ii) A boolean variable that declares convergence + Output: (i) The Hessian computed on the analytic center + (ii) The analytic center of the polytope + (iii) A boolean variable that declares convergence + + Note: Using MT as to deal with both dense and sparse matrices, MT_dense will be the type of result matrix */ -template -std::tuple analytic_center_linear_ineq(MT const& A, VT const& b, - unsigned int const max_iters = 500, - NT const grad_err_tol = 1e-08, - NT const rel_pos_err_tol = 1e-12) +template +std::tuple analytic_center_linear_ineq(MT const& A, VT const& b, VT const& x0, + unsigned int const max_iters = 500, + NT const grad_err_tol = 1e-08, + NT const rel_pos_err_tol = 1e-12) { - VT x; - bool feasibility_only = true, converged; - // Compute a feasible point - std::tie(x, std::ignore, converged) = max_inscribed_ball(A, b, max_iters, 1e-08, feasibility_only); - VT Ax = A * x; - if (!converged || (Ax.array() > b.array()).any()) - { - std::runtime_error("The computation of the analytic center failed."); - } // Initialization + VT x = x0; + VT Ax = A * x; const int n = A.cols(), m = A.rows(); MT H(n, n), A_trans = A.transpose(); VT grad(n), d(n), Ad(m), b_Ax(m), step_d(n), x_prev; NT grad_err, rel_pos_err, rel_pos_err_temp, step; unsigned int iter = 0; - converged = false; + bool converged = false; const NT tol_bnd = NT(0.01); + auto llt = initialize_chol(A_trans, A); get_hessian_grad_logbarrier(A, A_trans, b, x, Ax, H, grad, b_Ax); do { iter++; // Compute the direction - d.noalias() = - H.llt().solve(grad); + d.noalias() = - solve_vec(llt, H, grad); Ad.noalias() = A * d; // Compute the step length step = std::min((NT(1) - tol_bnd) * get_max_step(Ad, b_Ax), NT(1)); @@ -128,7 +118,17 @@ std::tuple analytic_center_linear_ineq(MT const& A, VT const& b, } } while (true); - return std::make_tuple(x, converged); + return std::make_tuple(MT_dense(H), x, converged); +} + +template +std::tuple analytic_center_linear_ineq(MT const& A, VT const& b, + unsigned int const max_iters = 500, + NT const grad_err_tol = 1e-08, + NT const rel_pos_err_tol = 1e-12) +{ + VT x0 = compute_feasible_point(A, b); + return analytic_center_linear_ineq(A, b, x0, max_iters, grad_err_tol, rel_pos_err_tol); } #endif diff --git a/include/preprocess/estimate_L_smooth_parameter.hpp b/include/preprocess/estimate_L_smooth_parameter.hpp index 8f5a1e89c..180f20947 100644 --- a/include/preprocess/estimate_L_smooth_parameter.hpp +++ b/include/preprocess/estimate_L_smooth_parameter.hpp @@ -1,7 +1,7 @@ // VolEsti (volume computation and sampling library) -// Copyright (c) 2012-2020 Vissarion Fisikopoulos -// Copyright (c) 2018-2020 Apostolos Chalkis +// Copyright (c) 2012-2024 Vissarion Fisikopoulos +// Copyright (c) 2018-2024 Apostolos Chalkis //Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. diff --git a/include/preprocess/feasible_point.hpp b/include/preprocess/feasible_point.hpp new file mode 100644 index 000000000..6e7f5db0a --- /dev/null +++ b/include/preprocess/feasible_point.hpp @@ -0,0 +1,34 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2024 Vissarion Fisikopoulos +// Copyright (c) 2024 Apostolos Chalkis +// Copyright (c) 2024 Elias Tsigaridas + +// Licensed under GNU LGPL.3, see LICENCE file + + +#ifndef FEASIBLE_POINT_HPP +#define FEASIBLE_POINT_HPP + +#include + +#include "preprocess/max_inscribed_ball.hpp" + +// Using MT as to deal with both dense and sparse matrices +template +VT compute_feasible_point(MT const& A, VT const& b) +{ + VT x; + bool feasibility_only = true, converged; + unsigned max_iters = 10000; + // Compute a feasible point + std::tie(x, std::ignore, converged) = max_inscribed_ball(A, b, max_iters, 1e-08, feasibility_only); + if (!converged || ((A * x).array() > b.array()).any()) + { + std::runtime_error("The computation of a feasible point failed."); + } + return x; +} + + +#endif diff --git a/include/preprocess/inscribed_ellipsoid_rounding.hpp b/include/preprocess/inscribed_ellipsoid_rounding.hpp new file mode 100644 index 000000000..dd20d533d --- /dev/null +++ b/include/preprocess/inscribed_ellipsoid_rounding.hpp @@ -0,0 +1,134 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2012-2024 Vissarion Fisikopoulos +// Copyright (c) 2018-2024 Apostolos Chalkis + +//Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. + +// Licensed under GNU LGPL.3, see LICENCE file + + +#ifndef INSCRIBED_ELLIPSOID_ROUNDING_HPP +#define INSCRIBED_ELLIPSOID_ROUNDING_HPP + +#include "preprocess/max_inscribed_ellipsoid.hpp" +#include "preprocess/analytic_center_linear_ineq.h" +#include "preprocess/feasible_point.hpp" + +enum EllipsoidType +{ + MAX_ELLIPSOID = 1, + LOG_BARRIER = 2 +}; + +template +inline static std::tuple +compute_inscribed_ellipsoid(Custom_MT A, VT b, VT const& x0, + unsigned int const& maxiter, + NT const& tol, NT const& reg) +{ + if constexpr (ellipsoid_type == EllipsoidType::MAX_ELLIPSOID) + { + return max_inscribed_ellipsoid(A, b, x0, maxiter, tol, reg); + } else if constexpr (ellipsoid_type == EllipsoidType::LOG_BARRIER) + { + return analytic_center_linear_ineq(A, b, x0); + } else + { + std::runtime_error("Unknown rounding method."); + } + return {}; +} + +template +< + typename MT, + typename VT, + typename NT, + typename Polytope, + int ellipsoid_type = EllipsoidType::MAX_ELLIPSOID +> +std::tuple inscribed_ellipsoid_rounding(Polytope &P, + unsigned int const max_iterations = 5, + NT const max_eig_ratio = NT(6)) +{ + typedef typename Polytope::PointType Point; + VT x = compute_feasible_point(P.get_mat(), P.get_vec()); + return inscribed_ellipsoid_rounding(P, Point(x), max_iterations, max_eig_ratio); +} + +template +< + typename MT, + typename VT, + typename NT, + typename Polytope, + typename Point, + int ellipsoid_type = EllipsoidType::MAX_ELLIPSOID +> +std::tuple inscribed_ellipsoid_rounding(Polytope &P, + Point const& InnerPoint, + unsigned int const max_iterations = 5, + NT const max_eig_ratio = NT(6)) +{ + unsigned int maxiter = 500, iter = 1, d = P.dimension(); + VT x0 = InnerPoint.getCoefficients(), center, shift = VT::Zero(d); + MT E, L, T = MT::Identity(d, d); + bool converged; + NT R = 100.0, r = 1.0, tol = std::pow(10, -6.0), reg = std::pow(10, -4.0), round_val = 1.0; + + while (true) + { + // Compute the desired inscribed ellipsoid in P + std::tie(E, center, converged) = + compute_inscribed_ellipsoid(P.get_mat(), P.get_vec(), x0, maxiter, tol, reg); + + E = (E + E.transpose()) / 2.0; + E += MT::Identity(d, d)*std::pow(10, -8.0); //normalize E + + Eigen::LLT lltOfA(E.llt().solve(MT::Identity(E.cols(), E.cols()))); // compute the Cholesky decomposition of E^{-1} + L = lltOfA.matrixL(); + + // Computing eigenvalues of E + Spectra::DenseSymMatProd op(E); + // The value of ncv is chosen empirically + Spectra::SymEigsSolver> eigs(&op, 2, std::min(std::max(10, int(d)/5), int(d))); + eigs.init(); + int nconv = eigs.compute(); + if (eigs.info() == Spectra::COMPUTATION_INFO::SUCCESSFUL) { + R = 1.0 / eigs.eigenvalues().coeff(1); + r = 1.0 / eigs.eigenvalues().coeff(0); + } else { + Eigen::SelfAdjointEigenSolver eigensolver(E); + if (eigensolver.info() == Eigen::ComputationInfo::Success) { + R = 1.0 / eigensolver.eigenvalues().coeff(0); + r = 1.0 / eigensolver.eigenvalues().template tail<1>().value(); + } else { + std::runtime_error("Computations failed."); + } + } + // Shift polytope and apply the linear transformation on P + P.shift(center); + shift.noalias() += T * center; + T.applyOnTheRight(L); // T = T * L; + round_val *= L.transpose().determinant(); + P.linear_transformIt(L); + + reg = std::max(reg / 10.0, std::pow(10, -10.0)); + P.normalize(); + x0 = VT::Zero(d); + + // Check the roundness of the polytope + if(((std::abs(R / r) <= max_eig_ratio && converged) || iter >= max_iterations)) { + break; + } + + iter++; + } + + std::tuple result = std::make_tuple(T, shift, std::abs(round_val)); + return result; +} + +#endif diff --git a/include/preprocess/mat_computational_operators.h b/include/preprocess/mat_computational_operators.h new file mode 100644 index 000000000..af200d221 --- /dev/null +++ b/include/preprocess/mat_computational_operators.h @@ -0,0 +1,316 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2024 Vissarion Fisikopoulos +// Copyright (c) 2024 Apostolos Chalkis +// Copyright (c) 2024 Elias Tsigaridas + +// Licensed under GNU LGPL.3, see LICENCE file + + +#ifndef MAT_COMPUTATIONAL_OPERATORS_H +#define MAT_COMPUTATIONAL_OPERATORS_H + +#include + +#include "Spectra/include/Spectra/SymEigsSolver.h" +#include "Spectra/include/Spectra/MatOp/DenseSymMatProd.h" +#include "Spectra/include/Spectra/MatOp/SparseSymMatProd.h" + + +template +struct AssertFalseType : std::false_type {}; + +template +inline static auto +initialize_chol(MT const& mat) +{ + using DenseMT = Eigen::Matrix; + using SparseMT = Eigen::SparseMatrix; + if constexpr (std::is_same::value) + { + return std::make_unique>(); + } else if constexpr (std::is_same::value) + { + auto llt = std::make_unique>(); + llt->analyzePattern(mat); + return llt; + } else + { + static_assert(AssertFalseType::value, + "Matrix type is not supported."); + } +} + +template +inline static auto +initialize_chol(MT const& A_trans, MT const& A) +{ + using DenseMT = Eigen::Matrix; + using SparseMT = Eigen::SparseMatrix; + if constexpr (std::is_same::value) + { + return std::make_unique>(); + } else if constexpr (std::is_same::value) + { + MT mat = A_trans * A; + return initialize_chol(mat); + } else + { + static_assert(AssertFalseType::value, + "Matrix type is not supported."); + } +} + +template +inline static VT solve_vec(std::unique_ptr const& llt, + MT const& H, VT const& b) +{ + using DenseMT = Eigen::Matrix; + using SparseMT = Eigen::SparseMatrix; + if constexpr (std::is_same::value) + { + llt->compute(H); + return llt->solve(b); + } else if constexpr (std::is_same::value) + { + llt->factorize(H); + return llt->solve(b); + } else + { + static_assert(AssertFalseType::value, + "Matrix type is not supported."); + } +} + +template +inline static Eigen::Matrix +solve_mat(std::unique_ptr const& llt, + MT const& H, MT const& mat, NT &logdetE) +{ + using DenseMT = Eigen::Matrix; + using SparseMT = Eigen::SparseMatrix; + if constexpr (std::is_same::value) + { + llt->compute(H); + logdetE = llt->matrixL().toDenseMatrix().diagonal().array().log().sum(); + return llt->solve(mat); + } else if constexpr (std::is_same::value) + { + llt->factorize(H); + logdetE = llt->matrixL().nestedExpression().diagonal().array().log().sum(); + return llt->solve(mat); + } else + { + static_assert(AssertFalseType::value, + "Matrix type is not supported."); + } +} + +template +inline static void update_Atrans_Diag_A(MT &H, MT const& A_trans, + MT const& A, diag_MT const& D) +{ + using DenseMT = Eigen::Matrix; + using SparseMT = Eigen::SparseMatrix; + if constexpr (std::is_same::value) + { + H.noalias() = A_trans * D * A; + } else if constexpr (std::is_same::value) + { + H = A_trans * D * A; + } else + { + static_assert(AssertFalseType::value, + "Matrix type is not supported."); + } +} + +template +inline static void update_Diag_A(MT &H, diag_MT const& D, MT const& A) +{ + using DenseMT = Eigen::Matrix; + using SparseMT = Eigen::SparseMatrix; + if constexpr (std::is_same::value) + { + H.noalias() = D * A; + } else if constexpr (std::is_same::value) + { + H = D * A; + } else + { + static_assert(AssertFalseType::value, + "Matrix type is not supported."); + } +} + +template +inline static void update_A_Diag(MT &H, MT const& A, diag_MT const& D) +{ + using DenseMT = Eigen::Matrix; + using SparseMT = Eigen::SparseMatrix; + if constexpr (std::is_same::value) + { + H.noalias() = A * D; + } else if constexpr (std::is_same::value) + { + H = A * D; + } else + { + static_assert(AssertFalseType::value, + "Matrix type is not supported."); + } +} + +template +inline static auto +get_mat_prod_op(MT const& E) +{ + using DenseMT = Eigen::Matrix; + using SparseMT = Eigen::SparseMatrix; + if constexpr (std::is_same::value) + { + return std::make_unique>(E); + } else if constexpr (std::is_same::value) + { + return std::make_unique>(E); + } else + { + static_assert(AssertFalseType::value, + "Matrix type is not supported."); + } +} + +template +inline static auto get_eigs_solver(std::unique_ptr const& op, int const n) +{ + using DenseMatProd = Spectra::DenseSymMatProd; + using SparseMatProd = Spectra::SparseSymMatProd; + if constexpr (std::is_same::value) + { + using SymDenseEigsSolver = Spectra::SymEigsSolver + < + NT, + Spectra::SELECT_EIGENVALUE::BOTH_ENDS, + DenseMatProd + >; + // The value of ncv is chosen empirically + return std::make_unique(op.get(), 2, std::min(std::max(10, n/5), n)); + } else if constexpr (std::is_same::value) + { + using SymSparseEigsSolver = Spectra::SymEigsSolver + < + NT, + Spectra::SELECT_EIGENVALUE::BOTH_ENDS, + SparseMatProd + >; + // The value of ncv is chosen empirically + return std::make_unique(op.get(), 2, std::min(std::max(10, n/5), n)); + } else + { + static_assert(AssertFalseType::value, + "Matrix-vector multiplication multiplication is not supported."); + } +} + +template +inline static void +init_Bmat(MT &B, int const n, MT const& A_trans, MT const& A) +{ + using DenseMT = Eigen::Matrix; + using SparseMT = Eigen::SparseMatrix; + if constexpr (std::is_same::value) + { + B.resize(n+1, n+1); + } else if constexpr (std::is_same::value) + { + // Initialize the structure of matrix B + typedef Eigen::Triplet triplet; + std::vector trp; + for (int i = 0; i < n; i++) + { + trp.push_back(triplet(i, i, NT(1))); + trp.push_back(triplet(i, n, NT(1))); + trp.push_back(triplet(n, i, NT(1))); + } + trp.push_back(triplet(n, n, NT(1))); + + MT ATA = A_trans * A; + for (int k=0; k::value, + "Matrix type is not supported."); + } +} + +template +inline static void +update_Bmat(MT &B, VT const& AtDe, VT const& d, + MT const& AtD, MT const& A) +{ + using DenseMT = Eigen::Matrix; + using SparseMT = Eigen::SparseMatrix; + const int n = A.cols(); + /* + B is (n+1)x(n+1) and AtD_A is nxn. + We set B(1:n), 1:n) = AtD_A, B(n+1, :) = AtD^T, B(:, n+1) = AtD, B(n+1, n+1) = d.sum() + */ + if constexpr (std::is_same::value) + { + B.block(0, 0, n, n).noalias() = AtD * A; + B.block(0, n, n, 1).noalias() = AtDe; + B.block(n, 0, 1, n).noalias() = AtDe.transpose(); + B(n, n) = d.sum(); + B.noalias() += 1e-14 * MT::Identity(n + 1, n + 1); + } else if constexpr (std::is_same::value) + { + MT AtD_A = AtD * A; + int k = 0; + while(k < B.outerSize()) + { + typename MT::InnerIterator it2(AtD_A, k <= n-1 ? k : k-1); + for (typename MT::InnerIterator it1(B, k); it1; ++it1) + { + if (it1.row() <= n-1 && it1.col() <= n-1) + { + it1.valueRef() = it2.value(); + } + else if (it1.row() == n && it1.col() <= n-1) + { + it1.valueRef() = AtDe.coeff(it1.col()); + } + else if (it1.col() == n && it1.row() <= n-1) + { + it1.valueRef() = AtDe.coeff(it1.row()); + } + else // then, (it1.row() == n && it1.col() == n) + { + it1.valueRef() = d.sum(); + } + + if (it1.row() == it1.col()) + { + it1.valueRef() += 1e-14; + } + if (it1.row()::value, + "Matrix type is not supported."); + } +} + + +#endif // MAT_COMPUTATIONAL_OPERATORS_H diff --git a/include/preprocess/max_inscribed_ball.hpp b/include/preprocess/max_inscribed_ball.hpp index 672be24e4..573813dd7 100644 --- a/include/preprocess/max_inscribed_ball.hpp +++ b/include/preprocess/max_inscribed_ball.hpp @@ -1,15 +1,17 @@ // VolEsti (volume computation and sampling library) -// Copyright (c) 2012-2020 Vissarion Fisikopoulos -// Copyright (c) 2018-2020 Apostolos Chalkis +// Copyright (c) 2012-2024 Vissarion Fisikopoulos +// Copyright (c) 2018-2024 Apostolos Chalkis //Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. // Licensed under GNU LGPL.3, see LICENCE file -#ifndef MAX_INNER_BALL -#define MAX_INNER_BALL +#ifndef MAX_INSCRIBED_BALL_HPP +#define MAX_INSCRIBED_BALL_HPP + +#include "preprocess/mat_computational_operators.h" /* This implmentation computes the largest inscribed ball in a given convex polytope P. @@ -26,10 +28,11 @@ radius r */ -template -void calcstep(MT const& A, MT const& A_trans, Eigen::LLT const& lltOfB, VT &s, - VT &y, VT &r1, VT const& r2, NT const& r3, VT &r4, - VT &dx, VT &ds, NT &dt, VT &dy, VT &tmp, VT &rhs) +template +void calcstep(MT const& A, MT const& A_trans, MT const& B, + llt_type const& llt, VT &s, VT &y, VT &r1, + VT const& r2, NT const& r3, VT &r4, VT &dx, + VT &ds, NT &dt, VT &dy, VT &tmp, VT &rhs) { int m = A.rows(), n = A.cols(); NT *vec_iter1 = tmp.data(), *vec_iter2 = y.data(), *vec_iter3 = s.data(), @@ -42,7 +45,7 @@ void calcstep(MT const& A, MT const& A_trans, Eigen::LLT const& lltOfB, VT & rhs.block(0,0,n,1).noalias() = r2 + A_trans * tmp; rhs(n) = r3 + tmp.sum(); - VT dxdt = lltOfB.solve(rhs); + VT dxdt = solve_vec(llt, B, rhs); dx = dxdt.block(0,0,n,1); dt = dxdt(n); @@ -56,12 +59,13 @@ void calcstep(MT const& A, MT const& A_trans, Eigen::LLT const& lltOfB, VT & } } - +// Using MT as to deal with both dense and sparse matrices template std::tuple max_inscribed_ball(MT const& A, VT const& b, unsigned int maxiter, NT tol, const bool feasibility_only = false) { + //typedef matrix_computational_operator mat_op; int m = A.rows(), n = A.cols(); bool converge = false; @@ -84,9 +88,10 @@ std::tuple max_inscribed_ball(MT const& A, VT const& b, NT const tau0 = 0.995, power_num = 5.0 * std::pow(10.0, 15.0); NT *vec_iter1, *vec_iter2, *vec_iter3, *vec_iter4; - MT B(n + 1, n + 1), AtD(n, m), - eEye = std::pow(10.0, -14.0) * MT::Identity(n + 1, n + 1), - A_trans = A.transpose(); + MT B, AtD(n, m), A_trans = A.transpose(); + + init_Bmat(B, n, A_trans, A); + auto llt = initialize_chol(B); for (unsigned int i = 0; i < maxiter; ++i) { @@ -135,20 +140,13 @@ std::tuple max_inscribed_ball(MT const& A, VT const& b, vec_iter3++; vec_iter2++; } - AtD.noalias() = A_trans*d.asDiagonal(); + update_A_Diag(AtD, A_trans, d.asDiagonal()); // AtD = A_trans*d.asDiagonal() AtDe.noalias() = AtD * e_m; - B.block(0, 0, n, n).noalias() = AtD * A; - B.block(0, n, n, 1).noalias() = AtDe; - B.block(n, 0, 1, n).noalias() = AtDe.transpose(); - B(n, n) = d.sum(); - B.noalias() += eEye; - - // Cholesky decomposition - Eigen::LLT lltOfB(B); + update_Bmat(B, AtDe, d, AtD, A); // predictor step & length - calcstep(A, A_trans, lltOfB, s, y, r1, r2, r3, r4, dx, ds, dt, dy, tmp, rhs); + calcstep(A, A_trans, B, llt, s, y, r1, r2, r3, r4, dx, ds, dt, dy, tmp, rhs); alphap = -1.0; alphad = -1.0; @@ -175,7 +173,7 @@ std::tuple max_inscribed_ball(MT const& A, VT const& b, // corrector and combined step & length mu_ds_dy.noalias() = e_m * mu - ds.cwiseProduct(dy); - calcstep(A, A_trans, lltOfB, s, y, o_m, o_n, 0.0, mu_ds_dy, dxc, dsc, dtc, dyc, tmp, rhs); + calcstep(A, A_trans, B, llt, s, y, o_m, o_n, 0.0, mu_ds_dy, dxc, dsc, dtc, dyc, tmp, rhs); dx += dxc; ds += dsc; @@ -216,4 +214,4 @@ std::tuple max_inscribed_ball(MT const& A, VT const& b, return result; } -#endif +#endif // MAX_INSCRIBED_BALL_HPP diff --git a/include/preprocess/max_inscribed_ellipsoid.hpp b/include/preprocess/max_inscribed_ellipsoid.hpp index 7a0c19a6f..f44f60ff0 100644 --- a/include/preprocess/max_inscribed_ellipsoid.hpp +++ b/include/preprocess/max_inscribed_ellipsoid.hpp @@ -15,9 +15,7 @@ #include #include -#include "Spectra/include/Spectra/SymEigsSolver.h" -#include "Spectra/include/Spectra/Util/SelectionRule.h" -#include "Spectra/include/Spectra/MatOp/DenseSymMatProd.h" +#include "preprocess/mat_computational_operators.h" /* @@ -37,14 +35,14 @@ matrix E2^{-1} = E_transpose * E */ -// using Custom_MT as to deal with both dense and sparse matrices, MT will be the type of result matrix -// TODO: Change the return data structure to std::tuple -template -std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT const& x0, - unsigned int const& maxiter, - NT const& tol, NT const& reg) +// Using MT as to deal with both dense and sparse matrices, MT_dense will be the type of result matrix +template +std::tuple max_inscribed_ellipsoid(MT A, VT b, VT const& x0, + unsigned int const& maxiter, + NT const& tol, NT const& reg) { typedef Eigen::DiagonalMatrix Diagonal_MT; + //typedef matrix_computational_operator mat_op; int m = A.rows(), n = A.cols(); bool converged = false; @@ -66,22 +64,23 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT VT const bmAx0 = b - A * x0, ones_m = VT::Ones(m); - MT Q(m, m), E2(n, n), YQ(m,m), G(m,m), T(m,n), ATP(n,m), ATP_A(n,n); + MT_dense Q(m,m), YQ(m,m), G(m,m), T(m,n), ATP(n,m), ATP_A(n,n); Diagonal_MT Y(m); - Custom_MT YA(m, n); + MT YA(m, n); A = (ones_m.cwiseProduct(bmAx0.cwiseInverse())).asDiagonal() * A, b = ones_m; - Custom_MT A_trans = A.transpose(); + MT A_trans = A.transpose(), E2(n,n); + + auto llt = initialize_chol(A_trans, A); int i = 1; while (i <= maxiter) { Y = y.asDiagonal(); - E2.noalias() = MT(A_trans * Y * A); - Eigen::LLT llt(E2); - - Q.noalias() = A * llt.solve(A_trans); + update_Atrans_Diag_A(E2, A_trans, A, Y); + Q.noalias() = A * solve_mat(llt, E2, A_trans, logdetE2); + h = Q.diagonal(); h = h.cwiseSqrt(); @@ -120,7 +119,6 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT res = std::max(r1, r2); res = std::max(res, r3); - logdetE2 = llt.matrixL().toDenseMatrix().diagonal().array().log().sum(); objval = logdetE2; //logdet of E2 is already divided by 2 if (i % 10 == 0) { @@ -128,15 +126,13 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT NT rel, Rel; // computing eigenvalues of E2 - Spectra::DenseSymMatProd op(E2); - // The value of ncv is chosen empirically - Spectra::SymEigsSolver> eigs(&op, 2, std::min(std::max(10, n/5), n)); - eigs.init(); - int nconv = eigs.compute(); - if (eigs.info() == Spectra::COMPUTATION_INFO::SUCCESSFUL) { - Rel = 1.0 / eigs.eigenvalues().coeff(1); - rel = 1.0 / eigs.eigenvalues().coeff(0); + auto op = get_mat_prod_op(E2); + auto eigs = get_eigs_solver(op, n); + eigs->init(); + int nconv = eigs->compute(); + if (eigs->info() == Spectra::COMPUTATION_INFO::SUCCESSFUL) { + Rel = 1.0 / eigs->eigenvalues().coeff(1); + rel = 1.0 / eigs->eigenvalues().coeff(0); } else { Eigen::SelfAdjointEigenSolver eigensolver(E2); // E2 is positive definite matrix if (eigensolver.info() == Eigen::ComputationInfo::Success) { @@ -176,7 +172,7 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT YQ.noalias() = Y * Q; G = YQ.cwiseProduct(YQ.transpose()); y2h = 2.0 * yh; - YA.noalias() = Y * A; + update_Diag_A(YA, Y, A); // YA = Y * A; vec_iter1 = y2h.data(); vec_iter2 = z.data(); @@ -190,10 +186,10 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT G.diagonal() += y2h_z; h_z = h + z; - Eigen::PartialPivLU luG(G); - T.noalias() = luG.solve(h_z.asDiagonal()*YA); + Eigen::PartialPivLU luG(G); + T.noalias() = luG.solve(MT_dense(h_z.asDiagonal()*YA)); - ATP.noalias() = MT(y2h.asDiagonal()*T - YA).transpose(); + ATP.noalias() = MT_dense(y2h.asDiagonal()*T - YA).transpose(); vec_iter1 = R3.data(); vec_iter2 = y.data(); @@ -256,7 +252,7 @@ std::pair, bool> max_inscribed_ellipsoid(Custom_MT A, VT b, VT x += x0; } - return std::pair, bool>(std::pair(E2, x), converged); + return std::make_tuple(E2, x, converged); } diff --git a/include/preprocess/max_inscribed_ellipsoid_rounding.hpp b/include/preprocess/max_inscribed_ellipsoid_rounding.hpp deleted file mode 100644 index da00a8867..000000000 --- a/include/preprocess/max_inscribed_ellipsoid_rounding.hpp +++ /dev/null @@ -1,94 +0,0 @@ -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2012-2020 Vissarion Fisikopoulos -// Copyright (c) 2018-2020 Apostolos Chalkis - -//Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. - -// Licensed under GNU LGPL.3, see LICENCE file - - -#ifndef MAX_ELLIPSOID_ROUNDING_HPP -#define MAX_ELLIPSOID_ROUNDING_HPP - -#include "max_inscribed_ellipsoid.hpp" - -template -< - typename MT, - typename VT, - typename NT, - typename Polytope, - typename Point -> -std::tuple max_inscribed_ellipsoid_rounding(Polytope &P, - Point const& InnerPoint, - unsigned int const max_iterations = 5, - NT const max_eig_ratio = NT(6)) -{ - std::pair, bool> iter_res; - iter_res.second = false; - - VT x0 = InnerPoint.getCoefficients(); - MT E, L; - unsigned int maxiter = 500, iter = 1, d = P.dimension(); - - NT R = 100.0, r = 1.0, tol = std::pow(10, -6.0), reg = std::pow(10, -4.0), round_val = 1.0; - - MT T = MT::Identity(d, d); - VT shift = VT::Zero(d); - - while (true) - { - // compute the largest inscribed ellipsoid in P centered at x0 - iter_res = max_inscribed_ellipsoid(P.get_mat(), P.get_vec(), x0, maxiter, tol, reg); - E = iter_res.first.first; - E = (E + E.transpose()) / 2.0; - E = E + MT::Identity(d, d)*std::pow(10, -8.0); //normalize E - - Eigen::LLT lltOfA(E.llt().solve(MT::Identity(E.cols(), E.cols()))); // compute the Cholesky decomposition of E^{-1} - L = lltOfA.matrixL(); - - // computing eigenvalues of E - Spectra::DenseSymMatProd op(E); - // The value of ncv is chosen empirically - Spectra::SymEigsSolver> eigs(&op, 2, std::min(std::max(10, int(d)/5), int(d))); - eigs.init(); - int nconv = eigs.compute(); - if (eigs.info() == Spectra::COMPUTATION_INFO::SUCCESSFUL) { - R = 1.0 / eigs.eigenvalues().coeff(1); - r = 1.0 / eigs.eigenvalues().coeff(0); - } else { - Eigen::SelfAdjointEigenSolver eigensolver(E); - if (eigensolver.info() == Eigen::ComputationInfo::Success) { - R = 1.0 / eigensolver.eigenvalues().coeff(0); - r = 1.0 / eigensolver.eigenvalues().template tail<1>().value(); - } else { - std::runtime_error("Computations failed."); - } - } - // shift polytope and apply the linear transformation on P - P.shift(iter_res.first.second); - shift += T * iter_res.first.second; - T = T * L; - round_val *= L.transpose().determinant(); - P.linear_transformIt(L); - - reg = std::max(reg / 10.0, std::pow(10, -10.0)); - P.normalize(); - x0 = VT::Zero(d); - - // check the roundness of the polytope - if(((std::abs(R / r) <= max_eig_ratio && iter_res.second) || iter >= max_iterations)){ - break; - } - - iter++; - } - - std::tuple result = std::make_tuple(T, shift, std::abs(round_val)); - return result; -} - -#endif diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index e17848fc6..cdda6087c 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -289,13 +289,17 @@ add_executable (volume_cb_vpoly_intersection_vpoly volume_cb_vpoly_intersection_ add_test(NAME volume_cb_vpoly_intersection_vpoly_random_vpoly_sphere COMMAND volume_cb_vpoly_intersection_vpoly -tc=random_vpoly_sphere) -add_executable (new_rounding_test new_rounding_test.cpp $) +add_executable (rounding_test rounding_test.cpp $) add_test(NAME test_round_min_ellipsoid - COMMAND new_rounding_test -tc=round_min_ellipsoid) + COMMAND rounding_test -tc=round_min_ellipsoid) add_test(NAME test_round_max_ellipsoid - COMMAND new_rounding_test -tc=round_max_ellipsoid) + COMMAND rounding_test -tc=round_max_ellipsoid) add_test(NAME test_round_svd - COMMAND new_rounding_test -tc=round_svd) + COMMAND rounding_test -tc=round_svd) +add_test(NAME test_round_log_barrier_test + COMMAND rounding_test -tc=round_log_barrier_test) +add_test(NAME round_max_ellipsoid_sparse + COMMAND rounding_test -tc=round_max_ellipsoid_sparse) @@ -357,6 +361,10 @@ add_test(NAME test_feasibility_point COMMAND test_internal_points -tc=test_feasibility_point) add_test(NAME test_analytic_center COMMAND test_internal_points -tc=test_analytic_center) +add_test(NAME test_max_ball_sparse + COMMAND test_internal_points -tc=test_max_ball_sparse) + + set(ADDITIONAL_FLAGS "-march=native -DSIMD_LEN=0 -DTIME_KEEPING") @@ -380,7 +388,7 @@ TARGET_LINK_LIBRARIES(volume_cb_vpolytope lp_solve coverage_config) TARGET_LINK_LIBRARIES(volume_cb_zonotopes lp_solve coverage_config) TARGET_LINK_LIBRARIES(volume_cb_vpoly_intersection_vpoly lp_solve coverage_config) TARGET_LINK_LIBRARIES(volume_cb_vpoly_intersection_vpoly lp_solve ${MKL_LINK} coverage_config) -TARGET_LINK_LIBRARIES(new_rounding_test lp_solve ${MKL_LINK} coverage_config) +TARGET_LINK_LIBRARIES(rounding_test lp_solve ${MKL_LINK} coverage_config) TARGET_LINK_LIBRARIES(mcmc_diagnostics_test lp_solve ${MKL_LINK} coverage_config) TARGET_LINK_LIBRARIES(sampling_test lp_solve ${MKL_LINK} coverage_config) TARGET_LINK_LIBRARIES(mmcs_test lp_solve ${MKL_LINK} coverage_config) diff --git a/test/max_ellipsoid_rounding_test.cpp b/test/max_ellipsoid_rounding_test.cpp index a9c25b8c1..edce7a7d2 100644 --- a/test/max_ellipsoid_rounding_test.cpp +++ b/test/max_ellipsoid_rounding_test.cpp @@ -22,7 +22,7 @@ #include "volume/volume_cooling_gaussians.hpp" #include "volume/volume_cooling_balls.hpp" -#include "preprocess/max_inscribed_ellipsoid_rounding.hpp" +#include "preprocess/inscribed_ellipsoid_rounding.hpp" #include "generators/known_polytope_generators.h" @@ -64,7 +64,7 @@ void rounding_test(Polytope &HP, RNGType rng(d); std::pair InnerBall = HP.ComputeInnerBall(); - std::tuple res = max_inscribed_ellipsoid_rounding(HP, InnerBall.first); + std::tuple res = inscribed_ellipsoid_rounding(HP, InnerBall.first); // Setup the parameters int walk_len = 10; diff --git a/test/new_rounding_test.cpp b/test/rounding_test.cpp similarity index 61% rename from test/new_rounding_test.cpp rename to test/rounding_test.cpp index ab17ed939..7e15c99a5 100644 --- a/test/new_rounding_test.cpp +++ b/test/rounding_test.cpp @@ -22,7 +22,7 @@ #include "volume/volume_cooling_balls.hpp" #include "preprocess/min_sampling_covering_ellipsoid_rounding.hpp" -#include "preprocess/max_inscribed_ellipsoid_rounding.hpp" +#include "preprocess/inscribed_ellipsoid_rounding.hpp" #include "preprocess/svd_rounding.hpp" #include "generators/known_polytope_generators.h" @@ -110,8 +110,83 @@ void rounding_max_ellipsoid_test(Polytope &HP, typedef BoostRandomNumberGenerator RNGType; RNGType rng(d); std::pair InnerBall = HP.ComputeInnerBall(); - std::tuple res = max_inscribed_ellipsoid_rounding(HP, InnerBall.first); + std::tuple res = inscribed_ellipsoid_rounding(HP, InnerBall.first); + // Setup the parameters + int walk_len = 1; + NT e = 0.1; + + // Estimate the volume + std::cout << "Number type: " << typeid(NT).name() << std::endl; + + NT volume = std::get<2>(res) * volume_cooling_balls(HP, e, walk_len).second; + test_values(volume, expectedBilliard, exact); +} + +template +void rounding_max_ellipsoid_sparse_test(double const& expectedBilliard, + double const& expected) +{ + typedef typename Polytope::PointType Point; + typedef typename Point::FT NT; + typedef typename Polytope::MT MT; + typedef typename Polytope::VT VT; + typedef typename Poset::RT RT; + typedef typename Poset::RV RV; + typedef Eigen::SparseMatrix SpMT; + + // Create Poset, 4 elements, a0 <= a1, a0 <= a2, a1 <= a3 + RV poset_data{{0, 1}, {0, 2}, {1, 3}}; + Poset poset(4, poset_data); + + // Initialize order polytope from the poset + OrderPolytope OP(poset); + OP.normalize(); + SpMT Asp = OP.get_mat(); + + + NT tol = 1e-08; + unsigned int maxiter = 500; + auto [center, radius, converged] = max_inscribed_ball(Asp, OP.get_vec(), maxiter, tol); + CHECK(OP.is_in(Point(center)) == -1); + auto [E, x0, round_val] = inscribed_ellipsoid_rounding(OP, Point(center)); + + MT A = MT(OP.get_mat()); + VT b = OP.get_vec(); + int d = OP.dimension(); + + Polytope HP(d, A, b); + + typedef BoostRandomNumberGenerator RNGType; + // Setup the parameters + int walk_len = 1; + NT e = 0.1; + + // Estimate the volume + std::cout << "Number type: " << typeid(NT).name() << std::endl; + + NT volume = round_val * volume_cooling_balls(HP, e, walk_len).second; + test_values(volume, expectedBilliard, expected); +} + +template +void rounding_log_barrier_test(Polytope &HP, + double const& expectedBall, + double const& expectedCDHR, + double const& expectedRDHR, + double const& expectedBilliard, + double const& exact) +{ + typedef typename Polytope::PointType Point; + typedef typename Point::FT NT; + typedef typename Polytope::MT MT; + typedef typename Polytope::VT VT; + + int d = HP.dimension(); + typedef BoostRandomNumberGenerator RNGType; + RNGType rng(d); + std::pair InnerBall = HP.ComputeInnerBall(); + std::tuple res = inscribed_ellipsoid_rounding(HP, InnerBall.first); // Setup the parameters int walk_len = 1; NT e = 0.1; @@ -164,11 +239,11 @@ void call_test_min_ellipsoid() { typedef HPolytope Hpolytope; Hpolytope P; - std::cout << "\n--- Testing rounding of H-skinny_cube5" << std::endl; + std::cout << "\n--- Testing min ellipsoid rounding of H-skinny_cube5" << std::endl; P = generate_skinny_cube(5); rounding_min_ellipsoid_test(P, 0, 3070.64, 3188.25, 3140.6, 3200.0); - std::cout << "\n--- Testing rounding of H-skinny_cube10" << std::endl; + std::cout << "\n--- Testing min ellipsoid rounding of H-skinny_cube10" << std::endl; P = generate_skinny_cube(10); rounding_min_ellipsoid_test(P, 0, 122550, 108426, 105003.0, 102400.0); @@ -182,11 +257,33 @@ void call_test_max_ellipsoid() { typedef HPolytope Hpolytope; Hpolytope P; - std::cout << "\n--- Testing rounding of H-skinny_cube5" << std::endl; + std::cout << "\n--- Testing max ellipsoid rounding of H-skinny_cube5" << std::endl; P = generate_skinny_cube(5); rounding_max_ellipsoid_test(P, 0, 3070.64, 3188.25, 3262.61, 3200.0); } +template +void call_test_max_ellipsoid_sparse() { + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef HPolytope Hpolytope; + + std::cout << "\n--- Testing max ellipsoid rounding of sparse Order Polytope" << std::endl; + rounding_max_ellipsoid_sparse_test(0.13979, 3070.64); +} + +template +void call_test_log_barrier() { + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef HPolytope Hpolytope; + Hpolytope P; + + std::cout << "\n--- Testing log-barrier rounding of H-skinny_cube5" << std::endl; + P = generate_skinny_cube(5); + rounding_log_barrier_test(P, 0, 3070.64, 3188.25, 3262.77, 3200.0); +} + template void call_test_svd() { @@ -195,7 +292,7 @@ void call_test_svd() { typedef HPolytope Hpolytope; Hpolytope P; - std::cout << "\n--- Testing rounding of H-skinny_cube5" << std::endl; + std::cout << "\n--- Testing SVD rounding of H-skinny_cube5" << std::endl; P = generate_skinny_cube(5); rounding_svd_test(P, 0, 3070.64, 3188.25, 3140.6, 3200.0); } @@ -209,6 +306,14 @@ TEST_CASE("round_max_ellipsoid") { call_test_max_ellipsoid(); } +TEST_CASE("round_max_ellipsoid_sparse") { + call_test_max_ellipsoid_sparse(); +} + +TEST_CASE("round_log_barrier_test") { + call_test_log_barrier(); +} + TEST_CASE("round_svd") { call_test_svd(); } diff --git a/test/test_internal_points.cpp b/test/test_internal_points.cpp index fa44e9baf..8d4df0240 100644 --- a/test/test_internal_points.cpp +++ b/test/test_internal_points.cpp @@ -22,6 +22,9 @@ #include "generators/known_polytope_generators.h" #include "generators/h_polytopes_generator.h" +#include "convex_bodies/orderpolytope.h" +#include "misc/poset.h" + template void call_test_max_ball() { typedef Cartesian Kernel; @@ -40,12 +43,52 @@ void call_test_max_ball() { NT tol = 1e-08; unsigned int maxiter = 500; auto [center, radius, converged] = max_inscribed_ball(P.get_mat(), P.get_vec(), maxiter, tol); - CHECK(P.is_in(Point(center)) == -1); CHECK(std::abs(radius - InnerBall.second) <= 1e-03); CHECK(converged); } +template +void call_test_max_ball_sparse() { + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef HPolytope Hpolytope; + typedef boost::mt19937 PolyRNGType; + typedef typename OrderPolytope::VT VT; + typedef typename OrderPolytope::MT MT; + typedef typename Poset::RT RT; + typedef typename Poset::RV RV; + typedef Eigen::SparseMatrix SpMT; + + // Create Poset, 4 elements, a0 <= a1, a0 <= a2, a1 <= a3 + RV poset_data{{0, 1}, {0, 2}, {1, 3}}; + Poset poset(4, poset_data); + + // Initialize order polytope from the poset + OrderPolytope OP(poset); + OP.normalize(); + SpMT Asp = OP.get_mat(); + MT A = MT(OP.get_mat()); + VT b = OP.get_vec(); + + std::cout << "\n--- Testing Chebychev ball for sparse order Polytope" << std::endl; + + NT tol = 1e-08; + unsigned int maxiter = 500; + auto [center, radius, converged] = max_inscribed_ball(Asp, b, maxiter, tol); + auto [center2, radius2, converged2] = max_inscribed_ball(A, b, maxiter, tol); + + VT center_(4); + center_ << 0.207107, 0.5, 0.593398, 0.792893; + + CHECK(OP.is_in(Point(center)) == -1); + auto [E, x0, round_val] = inscribed_ellipsoid_rounding(OP, Point(center)); + + CHECK((center - center_).norm() <= 1e-06); + CHECK(std::abs(radius - 0.207107) <= 1e-06); + CHECK(converged); +} + template void call_test_max_ball_feasibility() { typedef Cartesian Kernel; @@ -77,6 +120,7 @@ void call_test_analytic_center() { typedef typename Hpolytope::MT MT; typedef typename Hpolytope::VT VT; typedef boost::mt19937 PolyRNGType; + typedef Eigen::SparseMatrix SpMT; Hpolytope P; std::cout << "\n--- Testing analytic center for skinny H-polytope" << std::endl; @@ -85,13 +129,24 @@ void call_test_analytic_center() { P = skinny_random_hpoly(3, 15, pre_rounding, max_min_eig_ratio, 127); P.normalize(); - auto [analytic_center, converged] = analytic_center_linear_ineq(P.get_mat(), P.get_vec()); + auto [Hessian, analytic_center, converged] = analytic_center_linear_ineq(P.get_mat(), P.get_vec()); + SpMT Asp = P.get_mat().sparseView(); + auto [Hessian_sp, analytic_center2, converged2] = analytic_center_linear_ineq(Asp, P.get_vec()); + CHECK(P.is_in(Point(analytic_center)) == -1); CHECK(converged); CHECK(std::abs(analytic_center(0) + 4.75912) < 1e-04); CHECK(std::abs(analytic_center(1) + 4.28762) < 1e-04); CHECK(std::abs(analytic_center(2) - 7.54156) < 1e-04); + + CHECK(P.is_in(Point(analytic_center2)) == -1); + CHECK(converged2); + CHECK(std::abs(analytic_center(0) - analytic_center2(0)) < 1e-12); + CHECK(std::abs(analytic_center(1) - analytic_center2(1)) < 1e-12); + CHECK(std::abs(analytic_center(2) - analytic_center2(2)) < 1e-12); + + CHECK((Hessian - Hessian_sp).norm() < 1e-12); } TEST_CASE("test_max_ball") { @@ -105,3 +160,7 @@ TEST_CASE("test_feasibility_point") { TEST_CASE("test_analytic_center") { call_test_analytic_center(); } + +TEST_CASE("test_max_ball_sparse") { + call_test_max_ball_sparse(); +} From f1abc361c34be73ea2d83ce122fd385f26ca4365 Mon Sep 17 00:00:00 2001 From: atrayees <121290945+atrayees@users.noreply.github.com> Date: Fri, 5 Jul 2024 21:21:23 +0530 Subject: [PATCH 29/35] Function/is correlation (#315) * new function is_correlation_matrix * update example function call this example calls the is_correlation_matrix() function that is present in include/matrix_operations/EigenvaluesProblems.h * remove blank line * remove blank line * fix spaces, add function parameter --- examples/correlation_matrices/sampler.cpp | 7 ++++--- .../matrix_operations/EigenvaluesProblems.h | 18 ++++++++++++++++++ 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/examples/correlation_matrices/sampler.cpp b/examples/correlation_matrices/sampler.cpp index f18f90a1b..0655767ea 100644 --- a/examples/correlation_matrices/sampler.cpp +++ b/examples/correlation_matrices/sampler.cpp @@ -150,10 +150,11 @@ void correlation_matrix_uniform_sampling_MT(const unsigned int n, const unsigned std::cout << "Elapsed time : " << time << " (ms)" << std::endl; int valid_points = 0; + EigenvaluesProblems> solver; for(const auto& points : randPoints){ - if(is_correlation_matrix(points.mat)){ - valid_points++; - } + if(solver.is_correlation_matrix(points.mat)){ + valid_points++; + } } std::cout << "Number of valid points = " << valid_points << std::endl; diff --git a/include/matrix_operations/EigenvaluesProblems.h b/include/matrix_operations/EigenvaluesProblems.h index 178d91666..7bad48cbc 100644 --- a/include/matrix_operations/EigenvaluesProblems.h +++ b/include/matrix_operations/EigenvaluesProblems.h @@ -439,6 +439,24 @@ class EigenvaluesProblems, E return false; } + /// Check if a matrix is indeed a correlation matrix + /// return true if input matrix is found to be a correlation matrix + /// |param[in] matrix + bool is_correlation_matrix(const MT& matrix, const double tol = 1e-8){ + + //check if all the diagonal elements are ones + for (int i=0 ; i tol){ + return false; + } + } + + //check if the matrix is positive definite + if (isPositiveSemidefinite(matrix)) return true; + + return false; + } + /// Minimum positive eigenvalue of the generalized eigenvalue problem A - lB /// Use Eigen::GeneralizedSelfAdjointEigenSolver ges(B,A) (faster) /// \param[in] A: symmetric positive definite matrix From e6dd7fd148099eb8f9c0c485753675d236c0e29a Mon Sep 17 00:00:00 2001 From: Apostolos Chalkis Date: Mon, 8 Jul 2024 04:15:55 -0600 Subject: [PATCH 30/35] Implement rounding method using the volumetric barrier (#313) * generalize rounding loop * support sparse cholesky operator * complete sparse support in max_inscribed_ball * complete sparse support in preprocesing * add sparse tests * change main rounding function name * improve explaining comments * resolve PR comments * changing the dates in copyrights * use if constexpr instead of SNIFAE * update the examples to cpp17 * update to cpp17 order polytope example * fix templating in mat_computational_operators * fix templating errors and change header file to mat_computational_operators * first implementation of the volumetric barrier ellipsoid * add criterion for step_iter * restructure code that computes barriers' centers * remove unused comments * resolve PR comments * remove NT typename from max_step() --------- Co-authored-by: Apostolos Chalkis --- .../preprocess/analytic_center_linear_ineq.h | 134 ------------------ .../preprocess/barrier_center_ellipsoid.hpp | 115 +++++++++++++++ .../inscribed_ellipsoid_rounding.hpp | 12 +- include/preprocess/max_inscribed_ball.hpp | 2 +- .../preprocess/max_inscribed_ellipsoid.hpp | 2 +- ...perators.h => rounding_util_functions.hpp} | 98 ++++++++++++- test/CMakeLists.txt | 6 +- test/rounding_test.cpp | 46 ++++++ test/test_internal_points.cpp | 47 +++++- 9 files changed, 307 insertions(+), 155 deletions(-) delete mode 100644 include/preprocess/analytic_center_linear_ineq.h create mode 100644 include/preprocess/barrier_center_ellipsoid.hpp rename include/preprocess/{mat_computational_operators.h => rounding_util_functions.hpp} (76%) diff --git a/include/preprocess/analytic_center_linear_ineq.h b/include/preprocess/analytic_center_linear_ineq.h deleted file mode 100644 index 80d731f6c..000000000 --- a/include/preprocess/analytic_center_linear_ineq.h +++ /dev/null @@ -1,134 +0,0 @@ -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2024 Vissarion Fisikopoulos -// Copyright (c) 2024 Apostolos Chalkis -// Copyright (c) 2024 Elias Tsigaridas - -// Licensed under GNU LGPL.3, see LICENCE file - - -#ifndef ANALYTIC_CENTER_H -#define ANALYTIC_CENTER_H - -#include - -#include "preprocess/max_inscribed_ball.hpp" -#include "preprocess/feasible_point.hpp" -#include "preprocess/mat_computational_operators.h" - -template -NT get_max_step(VT const& Ad, VT const& b_Ax) -{ - const int m = Ad.size(); - NT max_element = std::numeric_limits::lowest(), max_element_temp; - for (int i = 0; i < m; i++) { - max_element_temp = Ad.coeff(i) / b_Ax.coeff(i); - if (max_element_temp > max_element) { - max_element = max_element_temp; - } - } - - return NT(1) / max_element; -} - -template -void get_hessian_grad_logbarrier(MT const& A, MT const& A_trans, VT const& b, - VT const& x, VT const& Ax, MT &H, VT &grad, VT &b_Ax) -{ - b_Ax.noalias() = b - Ax; - VT s = b_Ax.cwiseInverse(); - VT s_sq = s.cwiseProduct(s); - // Gradient of the log-barrier function - grad.noalias() = A_trans * s; - // Hessian of the log-barrier function - update_Atrans_Diag_A(H, A_trans, A, s_sq.asDiagonal()); -} - -/* - This implementation computes the analytic center of a polytope given - as a set of linear inequalities P = {x | Ax <= b}. The analytic center - is the minimizer of the log barrier function i.e., the optimal solution - of the following optimization problem (Convex Optimization, Boyd and Vandenberghe, Section 8.5.3), - - \min -\sum \log(b_i - a_i^Tx), where a_i is the i-th row of A. - - The function solves the problem by using the Newton method. - - Input: (i) Matrix A, vector b such that the polytope P = {x | Ax<=b} - (ii) The number of maximum iterations, max_iters - (iii) Tolerance parameter grad_err_tol to bound the L2-norm of the gradient - (iv) Tolerance parameter rel_pos_err_tol to check the relative progress in each iteration - - Output: (i) The Hessian computed on the analytic center - (ii) The analytic center of the polytope - (iii) A boolean variable that declares convergence - - Note: Using MT as to deal with both dense and sparse matrices, MT_dense will be the type of result matrix -*/ -template -std::tuple analytic_center_linear_ineq(MT const& A, VT const& b, VT const& x0, - unsigned int const max_iters = 500, - NT const grad_err_tol = 1e-08, - NT const rel_pos_err_tol = 1e-12) -{ - // Initialization - VT x = x0; - VT Ax = A * x; - const int n = A.cols(), m = A.rows(); - MT H(n, n), A_trans = A.transpose(); - VT grad(n), d(n), Ad(m), b_Ax(m), step_d(n), x_prev; - NT grad_err, rel_pos_err, rel_pos_err_temp, step; - unsigned int iter = 0; - bool converged = false; - const NT tol_bnd = NT(0.01); - - auto llt = initialize_chol(A_trans, A); - get_hessian_grad_logbarrier(A, A_trans, b, x, Ax, H, grad, b_Ax); - - do { - iter++; - // Compute the direction - d.noalias() = - solve_vec(llt, H, grad); - Ad.noalias() = A * d; - // Compute the step length - step = std::min((NT(1) - tol_bnd) * get_max_step(Ad, b_Ax), NT(1)); - step_d.noalias() = step*d; - x_prev = x; - x += step_d; - Ax.noalias() += step*Ad; - - // Compute the max_i\{ |step*d_i| ./ |x_i| \} - rel_pos_err = std::numeric_limits::lowest(); - for (int i = 0; i < n; i++) - { - rel_pos_err_temp = std::abs(step_d.coeff(i) / x_prev.coeff(i)); - if (rel_pos_err_temp > rel_pos_err) - { - rel_pos_err = rel_pos_err_temp; - } - } - - get_hessian_grad_logbarrier(A, A_trans, b, x, Ax, H, grad, b_Ax); - grad_err = grad.norm(); - - if (iter >= max_iters || grad_err <= grad_err_tol || rel_pos_err <= rel_pos_err_tol) - { - converged = true; - break; - } - } while (true); - - return std::make_tuple(MT_dense(H), x, converged); -} - -template -std::tuple analytic_center_linear_ineq(MT const& A, VT const& b, - unsigned int const max_iters = 500, - NT const grad_err_tol = 1e-08, - NT const rel_pos_err_tol = 1e-12) -{ - VT x0 = compute_feasible_point(A, b); - return analytic_center_linear_ineq(A, b, x0, max_iters, grad_err_tol, rel_pos_err_tol); -} - -#endif diff --git a/include/preprocess/barrier_center_ellipsoid.hpp b/include/preprocess/barrier_center_ellipsoid.hpp new file mode 100644 index 000000000..0ab8c0457 --- /dev/null +++ b/include/preprocess/barrier_center_ellipsoid.hpp @@ -0,0 +1,115 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2024 Vissarion Fisikopoulos +// Copyright (c) 2024 Apostolos Chalkis +// Copyright (c) 2024 Elias Tsigaridas + +// Licensed under GNU LGPL.3, see LICENCE file + + +#ifndef BARRIER_CENTER_ELLIPSOID_HPP +#define BARRIER_CENTER_ELLIPSOID_HPP + +#include + +#include "preprocess/max_inscribed_ball.hpp" +#include "preprocess/feasible_point.hpp" +#include "preprocess/rounding_util_functions.hpp" + +/* + This implementation computes the analytic or the volumetric center of a polytope given + as a set of linear inequalities P = {x | Ax <= b}. The analytic center is the tminimizer + of the log barrier function, i.e., the optimal solution + of the following optimization problem (Convex Optimization, Boyd and Vandenberghe, Section 8.5.3), + + \min -\sum \log(b_i - a_i^Tx), where a_i is the i-th row of A. + + The volumetric center is the minimizer of the volumetric barrier function, i.e., the optimal + solution of the following optimization problem, + + \min logdet \nabla^2 f(x), where f(x) the log barrier function + + The function solves the problems by using the Newton method. + + Input: (i) Matrix A, vector b such that the polytope P = {x | Ax<=b} + (ii) The number of maximum iterations, max_iters + (iii) Tolerance parameter grad_err_tol to bound the L2-norm of the gradient + (iv) Tolerance parameter rel_pos_err_tol to check the relative progress in each iteration + + Output: (i) The Hessian of the barrier function + (ii) The analytic/volumetric center of the polytope + (iii) A boolean variable that declares convergence + + Note: Using MT as to deal with both dense and sparse matrices, MT_dense will be the type of result matrix +*/ +template +std::tuple barrier_center_ellipsoid_linear_ineq(MT const& A, VT const& b, VT const& x0, + unsigned int const max_iters = 500, + NT const grad_err_tol = 1e-08, + NT const rel_pos_err_tol = 1e-12) +{ + // Initialization + VT x = x0; + VT Ax = A * x; + const int n = A.cols(), m = A.rows(); + MT H(n, n), A_trans = A.transpose(); + VT grad(n), d(n), Ad(m), b_Ax(m), step_d(n), x_prev; + NT grad_err, rel_pos_err, rel_pos_err_temp, step, obj_val, obj_val_prev; + unsigned int iter = 0; + bool converged = false; + const NT tol_bnd = NT(0.01), tol_obj = NT(1e-06); + + auto [step_iter, max_step_multiplier] = init_step(); + auto llt = initialize_chol(A_trans, A); + get_barrier_hessian_grad(A, A_trans, b, x, Ax, llt, + H, grad, b_Ax, obj_val); + do { + iter++; + // Compute the direction + d.noalias() = - solve_vec(llt, H, grad); + Ad.noalias() = A * d; + // Compute the step length + step = std::min(max_step_multiplier * get_max_step(Ad, b_Ax), step_iter); + step_d.noalias() = step*d; + x_prev = x; + x += step_d; + Ax.noalias() += step*Ad; + + // Compute the max_i\{ |step*d_i| ./ |x_i| \} + rel_pos_err = std::numeric_limits::lowest(); + for (int i = 0; i < n; i++) + { + rel_pos_err_temp = std::abs(step_d.coeff(i) / x_prev.coeff(i)); + if (rel_pos_err_temp > rel_pos_err) + { + rel_pos_err = rel_pos_err_temp; + } + } + + obj_val_prev = obj_val; + get_barrier_hessian_grad(A, A_trans, b, x, Ax, llt, + H, grad, b_Ax, obj_val); + grad_err = grad.norm(); + + if (iter >= max_iters || grad_err <= grad_err_tol || rel_pos_err <= rel_pos_err_tol) + { + converged = true; + break; + } + get_step_next_iteration(obj_val_prev, obj_val, tol_obj, step_iter); + } while (true); + + return std::make_tuple(MT_dense(H), x, converged); +} + +template +std::tuple barrier_center_ellipsoid_linear_ineq(MT const& A, VT const& b, + unsigned int const max_iters = 500, + NT const grad_err_tol = 1e-08, + NT const rel_pos_err_tol = 1e-12) +{ + VT x0 = compute_feasible_point(A, b); + return barrier_center_ellipsoid_linear_ineq(A, b, x0, max_iters, grad_err_tol, rel_pos_err_tol); +} + +#endif // BARRIER_CENTER_ELLIPSOID_HPP diff --git a/include/preprocess/inscribed_ellipsoid_rounding.hpp b/include/preprocess/inscribed_ellipsoid_rounding.hpp index dd20d533d..37468b9d9 100644 --- a/include/preprocess/inscribed_ellipsoid_rounding.hpp +++ b/include/preprocess/inscribed_ellipsoid_rounding.hpp @@ -12,14 +12,9 @@ #define INSCRIBED_ELLIPSOID_ROUNDING_HPP #include "preprocess/max_inscribed_ellipsoid.hpp" -#include "preprocess/analytic_center_linear_ineq.h" +#include "preprocess/barrier_center_ellipsoid.hpp" #include "preprocess/feasible_point.hpp" -enum EllipsoidType -{ - MAX_ELLIPSOID = 1, - LOG_BARRIER = 2 -}; template inline static std::tuple @@ -30,9 +25,10 @@ compute_inscribed_ellipsoid(Custom_MT A, VT b, VT const& x0, if constexpr (ellipsoid_type == EllipsoidType::MAX_ELLIPSOID) { return max_inscribed_ellipsoid(A, b, x0, maxiter, tol, reg); - } else if constexpr (ellipsoid_type == EllipsoidType::LOG_BARRIER) + } else if constexpr (ellipsoid_type == EllipsoidType::LOG_BARRIER || + ellipsoid_type == EllipsoidType::VOLUMETRIC_BARRIER) { - return analytic_center_linear_ineq(A, b, x0); + return barrier_center_ellipsoid_linear_ineq(A, b, x0); } else { std::runtime_error("Unknown rounding method."); diff --git a/include/preprocess/max_inscribed_ball.hpp b/include/preprocess/max_inscribed_ball.hpp index 573813dd7..5f95970ba 100644 --- a/include/preprocess/max_inscribed_ball.hpp +++ b/include/preprocess/max_inscribed_ball.hpp @@ -11,7 +11,7 @@ #ifndef MAX_INSCRIBED_BALL_HPP #define MAX_INSCRIBED_BALL_HPP -#include "preprocess/mat_computational_operators.h" +#include "preprocess/rounding_util_functions.hpp" /* This implmentation computes the largest inscribed ball in a given convex polytope P. diff --git a/include/preprocess/max_inscribed_ellipsoid.hpp b/include/preprocess/max_inscribed_ellipsoid.hpp index f44f60ff0..cc65006b9 100644 --- a/include/preprocess/max_inscribed_ellipsoid.hpp +++ b/include/preprocess/max_inscribed_ellipsoid.hpp @@ -15,7 +15,7 @@ #include #include -#include "preprocess/mat_computational_operators.h" +#include "preprocess/rounding_util_functions.hpp" /* diff --git a/include/preprocess/mat_computational_operators.h b/include/preprocess/rounding_util_functions.hpp similarity index 76% rename from include/preprocess/mat_computational_operators.h rename to include/preprocess/rounding_util_functions.hpp index af200d221..6b94ace8b 100644 --- a/include/preprocess/mat_computational_operators.h +++ b/include/preprocess/rounding_util_functions.hpp @@ -1,14 +1,15 @@ // VolEsti (volume computation and sampling library) -// Copyright (c) 2024 Vissarion Fisikopoulos -// Copyright (c) 2024 Apostolos Chalkis -// Copyright (c) 2024 Elias Tsigaridas +// Copyright (c) 2012-2024 Vissarion Fisikopoulos +// Copyright (c) 2018-2024 Apostolos Chalkis + +//Contributed and/or modified by Alexandros Manochis, as part of Google Summer of Code 2020 program. // Licensed under GNU LGPL.3, see LICENCE file -#ifndef MAT_COMPUTATIONAL_OPERATORS_H -#define MAT_COMPUTATIONAL_OPERATORS_H +#ifndef ROUNDING_UTIL_FUNCTIONS_HPP +#define ROUNDING_UTIL_FUNCTIONS_HPP #include @@ -17,9 +18,35 @@ #include "Spectra/include/Spectra/MatOp/SparseSymMatProd.h" +enum EllipsoidType +{ + MAX_ELLIPSOID = 1, + LOG_BARRIER = 2, + VOLUMETRIC_BARRIER = 3 +}; + +template +struct AssertBarrierFalseType : std::false_type {}; + template struct AssertFalseType : std::false_type {}; +template +auto get_max_step(VT const& Ad, VT const& b_Ax) +{ + using NT = typename VT::Scalar; + const int m = Ad.size(); + NT max_element = std::numeric_limits::lowest(), max_element_temp; + for (int i = 0; i < m; i++) { + max_element_temp = Ad.coeff(i) / b_Ax.coeff(i); + if (max_element_temp > max_element) { + max_element = max_element_temp; + } + } + + return NT(1) / max_element; +} + template inline static auto initialize_chol(MT const& mat) @@ -312,5 +339,64 @@ update_Bmat(MT &B, VT const& AtDe, VT const& d, } } +template +std::tuple init_step() +{ + if constexpr (BarrierType == EllipsoidType::LOG_BARRIER) + { + return {NT(1), NT(0.99)}; + } else if constexpr (BarrierType == EllipsoidType::VOLUMETRIC_BARRIER) + { + return {NT(0.5), NT(0.4)}; + } else { + static_assert(AssertBarrierFalseType::value, + "Barrier type is not supported."); + } +} + +template +void get_barrier_hessian_grad(MT const& A, MT const& A_trans, VT const& b, + VT const& x, VT const& Ax, llt_type const& llt, + MT &H, VT &grad, VT &b_Ax, NT &obj_val) +{ + b_Ax.noalias() = b - Ax; + VT s = b_Ax.cwiseInverse(); + VT s_sq = s.cwiseProduct(s); + // Hessian of the log-barrier function + update_Atrans_Diag_A(H, A_trans, A, s_sq.asDiagonal()); + if constexpr (BarrierType == EllipsoidType::LOG_BARRIER) + { + grad.noalias() = A_trans * s; + } else if constexpr (BarrierType == EllipsoidType::VOLUMETRIC_BARRIER) + { + // Computing sigma(x)_i = (a_i^T H^{-1} a_i) / (b_i - a_i^Tx)^2 + MT_dense HA = solve_mat(llt, H, A_trans, obj_val); + MT_dense aiHai = HA.transpose().cwiseProduct(A); + VT sigma = (aiHai.rowwise().sum()).cwiseProduct(s_sq); + // Gradient of the volumetric barrier function + grad.noalias() = A_trans * (s.cwiseProduct(sigma)); + // Hessian of the volumetric barrier function + update_Atrans_Diag_A(H, A_trans, A, s_sq.cwiseProduct(sigma).asDiagonal()); + } else { + static_assert(AssertBarrierFalseType::value, + "Barrier type is not supported."); + } +} + +template +void get_step_next_iteration(NT const obj_val_prev, NT const obj_val, + NT const tol_obj, NT &step_iter) +{ + if constexpr (BarrierType == EllipsoidType::LOG_BARRIER) + { + step_iter = NT(1); + } else if constexpr (BarrierType == EllipsoidType::VOLUMETRIC_BARRIER) + { + step_iter *= (obj_val_prev <= obj_val - tol_obj) ? NT(0.9) : NT(0.999); + } else { + static_assert(AssertBarrierFalseType::value, + "Barrier type is not supported."); + } +} -#endif // MAT_COMPUTATIONAL_OPERATORS_H +#endif // ROUNDING_UTIL_FUNCTIONS_HPP \ No newline at end of file diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index cdda6087c..b524403ce 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -300,6 +300,8 @@ add_test(NAME test_round_log_barrier_test COMMAND rounding_test -tc=round_log_barrier_test) add_test(NAME round_max_ellipsoid_sparse COMMAND rounding_test -tc=round_max_ellipsoid_sparse) +add_test(NAME round_volumetric_barrier_test + COMMAND rounding_test -tc=round_volumetric_barrier_test) @@ -363,7 +365,9 @@ add_test(NAME test_analytic_center COMMAND test_internal_points -tc=test_analytic_center) add_test(NAME test_max_ball_sparse COMMAND test_internal_points -tc=test_max_ball_sparse) - +add_test(NAME test_volumetric_center + COMMAND test_internal_points -tc=test_volumetric_center) + set(ADDITIONAL_FLAGS "-march=native -DSIMD_LEN=0 -DTIME_KEEPING") diff --git a/test/rounding_test.cpp b/test/rounding_test.cpp index 7e15c99a5..3817cebb3 100644 --- a/test/rounding_test.cpp +++ b/test/rounding_test.cpp @@ -198,6 +198,36 @@ void rounding_log_barrier_test(Polytope &HP, test_values(volume, expectedBilliard, exact); } +template +void rounding_volumetric_barrier_test(Polytope &HP, + double const& expectedBall, + double const& expectedCDHR, + double const& expectedRDHR, + double const& expectedBilliard, + double const& exact) +{ + typedef typename Polytope::PointType Point; + typedef typename Point::FT NT; + typedef typename Polytope::MT MT; + typedef typename Polytope::VT VT; + + int d = HP.dimension(); + + typedef BoostRandomNumberGenerator RNGType; + RNGType rng(d); + std::pair InnerBall = HP.ComputeInnerBall(); + std::tuple res = inscribed_ellipsoid_rounding(HP, InnerBall.first); + // Setup the parameters + int walk_len = 1; + NT e = 0.1; + + // Estimate the volume + std::cout << "Number type: " << typeid(NT).name() << std::endl; + + NT volume = std::get<2>(res) * volume_cooling_balls(HP, e, walk_len).second; + test_values(volume, expectedBilliard, exact); +} + template void rounding_svd_test(Polytope &HP, @@ -284,6 +314,18 @@ void call_test_log_barrier() { rounding_log_barrier_test(P, 0, 3070.64, 3188.25, 3262.77, 3200.0); } +template +void call_test_volumetric_barrier() { + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef HPolytope Hpolytope; + Hpolytope P; + + std::cout << "\n--- Testing volumetric barrier rounding of H-skinny_cube5" << std::endl; + P = generate_skinny_cube(5); + rounding_volumetric_barrier_test(P, 0, 3070.64, 3188.25, 3262.77, 3200.0); +} + template void call_test_svd() { @@ -314,6 +356,10 @@ TEST_CASE("round_log_barrier_test") { call_test_log_barrier(); } +TEST_CASE("round_volumetric_barrier_test") { + call_test_volumetric_barrier(); +} + TEST_CASE("round_svd") { call_test_svd(); } diff --git a/test/test_internal_points.cpp b/test/test_internal_points.cpp index 8d4df0240..50965128c 100644 --- a/test/test_internal_points.cpp +++ b/test/test_internal_points.cpp @@ -17,7 +17,7 @@ #include "convex_bodies/hpolytope.h" #include "preprocess/max_inscribed_ball.hpp" -#include "preprocess/analytic_center_linear_ineq.h" +#include "preprocess/barrier_center_ellipsoid.hpp" #include "generators/known_polytope_generators.h" #include "generators/h_polytopes_generator.h" @@ -129,10 +129,10 @@ void call_test_analytic_center() { P = skinny_random_hpoly(3, 15, pre_rounding, max_min_eig_ratio, 127); P.normalize(); - auto [Hessian, analytic_center, converged] = analytic_center_linear_ineq(P.get_mat(), P.get_vec()); - SpMT Asp = P.get_mat().sparseView(); + auto [Hessian, analytic_center, converged] = barrier_center_ellipsoid_linear_ineq(P.get_mat(), P.get_vec()); - auto [Hessian_sp, analytic_center2, converged2] = analytic_center_linear_ineq(Asp, P.get_vec()); + SpMT Asp = P.get_mat().sparseView(); + auto [Hessian_sp, analytic_center2, converged2] = barrier_center_ellipsoid_linear_ineq(Asp, P.get_vec()); CHECK(P.is_in(Point(analytic_center)) == -1); CHECK(converged); @@ -149,6 +149,41 @@ void call_test_analytic_center() { CHECK((Hessian - Hessian_sp).norm() < 1e-12); } +template +void call_test_volumetric_center() { + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef HPolytope Hpolytope; + typedef typename Hpolytope::MT MT; + typedef typename Hpolytope::VT VT; + typedef boost::mt19937 PolyRNGType; + typedef Eigen::SparseMatrix SpMT; + Hpolytope P; + + std::cout << "\n--- Testing volumetric center for skinny H-polytope" << std::endl; + bool pre_rounding = true; // round random polytope before applying the skinny transformation + NT max_min_eig_ratio = NT(100); + P = skinny_random_hpoly(3, 15, pre_rounding, max_min_eig_ratio, 127); + P.normalize(); + + auto [Hessian, volumetric_center, converged] = barrier_center_ellipsoid_linear_ineq(P.get_mat(), P.get_vec()); + SpMT Asp = P.get_mat().sparseView(); + auto [Hessian_sp, volumetric_center2, converged2] = barrier_center_ellipsoid_linear_ineq(Asp, P.get_vec()); + CHECK(P.is_in(Point(volumetric_center)) == -1); + CHECK(converged); + CHECK(std::abs(volumetric_center(0) + 1.49031) < 1e-04); + CHECK(std::abs(volumetric_center(1) + 1.51709) < 1e-04); + CHECK(std::abs(volumetric_center(2) - 2.49381) < 1e-04); + + CHECK(P.is_in(Point(volumetric_center2)) == -1); + CHECK(converged2); + CHECK(std::abs(volumetric_center(0) - volumetric_center2(0)) < 1e-12); + CHECK(std::abs(volumetric_center(1) - volumetric_center2(1)) < 1e-12); + CHECK(std::abs(volumetric_center(2) - volumetric_center2(2)) < 1e-12); + + CHECK((Hessian - Hessian_sp).norm() < 1e-12); +} + TEST_CASE("test_max_ball") { call_test_max_ball(); } @@ -161,6 +196,10 @@ TEST_CASE("test_analytic_center") { call_test_analytic_center(); } +TEST_CASE("test_volumetric_center") { + call_test_volumetric_center(); +} + TEST_CASE("test_max_ball_sparse") { call_test_max_ball_sparse(); } From 5b6288f60a056eaad5f11799fc8a9596908fc376 Mon Sep 17 00:00:00 2001 From: lucaperju Date: Thu, 11 Jul 2024 14:39:13 +0200 Subject: [PATCH 31/35] New gaussian accelerated billiard walk --- include/convex_bodies/hpolytope.h | 14 +- include/misc/poset.h | 1 + .../preprocess/analytic_center_linear_ineq.h | 134 +++++++++ ...new_gaussian_accelerated_billiard_walk.hpp | 277 ++++++++++++++++++ include/random_walks/random_walks.hpp | 1 + .../uniform_accelerated_billiard_walk.hpp | 17 +- include/sampling/random_point_generators.hpp | 2 +- test/CMakeLists.txt | 19 +- test/new_rounding_test.cpp | 1 - test/sampling_test.cpp | 53 ++++ 10 files changed, 505 insertions(+), 14 deletions(-) create mode 100644 include/preprocess/analytic_center_linear_ineq.h create mode 100644 include/random_walks/new_gaussian_accelerated_billiard_walk.hpp diff --git a/include/convex_bodies/hpolytope.h b/include/convex_bodies/hpolytope.h index 49d942827..909fcddce 100644 --- a/include/convex_bodies/hpolytope.h +++ b/include/convex_bodies/hpolytope.h @@ -541,7 +541,6 @@ class HPolytope { NT lamda = 0; VT sum_nom; - unsigned int j; int m = num_of_hyperplanes(), facet; Ar.noalias() += lambda_prev*Av; @@ -906,6 +905,19 @@ class HPolytope { v += a; } + template + NT compute_reflection(Point &v, const Point &, MT const &AE, VT const &AEA, NT const &vEv, update_parameters const ¶ms) const { + + Point a((-2.0 * params.inner_vi_ak) * A.row(params.facet_prev)); + VT x = v.getCoefficients(); + NT new_vEv = vEv - (4.0 * params.inner_vi_ak) * (AE.row(params.facet_prev).dot(x)) + + (4.0 * params.inner_vi_ak * params.inner_vi_ak) * AEA(params.facet_prev); + v += a; + NT coeff = std::sqrt(vEv / new_vEv); + v = v * coeff; + return coeff; + } + void update_position_internal(NT&){} template diff --git a/include/misc/poset.h b/include/misc/poset.h index 69ba02ec7..17af82306 100644 --- a/include/misc/poset.h +++ b/include/misc/poset.h @@ -5,6 +5,7 @@ // Copyright (c) 2020-2021 Vaibhav Thakkar // Contributed and/or modified by Vaibhav Thakkar, as part of Google Summer of Code 2021 program. +// Contributed and/or modified by Luca Perju, as part of Google Summer of Code 2024 program. // Licensed under GNU LGPL.3, see LICENCE file diff --git a/include/preprocess/analytic_center_linear_ineq.h b/include/preprocess/analytic_center_linear_ineq.h new file mode 100644 index 000000000..4a4852ac0 --- /dev/null +++ b/include/preprocess/analytic_center_linear_ineq.h @@ -0,0 +1,134 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2024 Vissarion Fisikopoulos +// Copyright (c) 2024 Apostolos Chalkis +// Copyright (c) 2024 Elias Tsigaridas + +// Licensed under GNU LGPL.3, see LICENCE file + + +#ifndef ANALYTIC_CENTER_LINEAR_INEQ_H +#define ANALYTIC_CENTER_LINEAR_INEQ_H + +#include + +#include "preprocess/max_inscribed_ball.hpp" +#include "preprocess/feasible_point.hpp" +#include "preprocess/mat_computational_operators.h" + +template +NT get_max_step(VT const& Ad, VT const& b_Ax) +{ + const int m = Ad.size(); + NT max_element = std::numeric_limits::lowest(), max_element_temp; + for (int i = 0; i < m; i++) { + max_element_temp = Ad.coeff(i) / b_Ax.coeff(i); + if (max_element_temp > max_element) { + max_element = max_element_temp; + } + } + + return NT(1) / max_element; +} + +template +void get_hessian_grad_logbarrier(MT const& A, MT const& A_trans, VT const& b, + VT const& x, VT const& Ax, MT &H, VT &grad, VT &b_Ax) +{ + b_Ax.noalias() = b - Ax; + VT s = b_Ax.cwiseInverse(); + VT s_sq = s.cwiseProduct(s); + // Gradient of the log-barrier function + grad.noalias() = A_trans * s; + // Hessian of the log-barrier function + update_Atrans_Diag_A(H, A_trans, A, s_sq.asDiagonal()); +} + +/* + This implementation computes the analytic center of a polytope given + as a set of linear inequalities P = {x | Ax <= b}. The analytic center + is the minimizer of the log barrier function i.e., the optimal solution + of the following optimization problem (Convex Optimization, Boyd and Vandenberghe, Section 8.5.3), + + \min -\sum \log(b_i - a_i^Tx), where a_i is the i-th row of A. + + The function solves the problem by using the Newton method. + + Input: (i) Matrix A, vector b such that the polytope P = {x | Ax<=b} + (ii) The number of maximum iterations, max_iters + (iii) Tolerance parameter grad_err_tol to bound the L2-norm of the gradient + (iv) Tolerance parameter rel_pos_err_tol to check the relative progress in each iteration + + Output: (i) The Hessian computed on the analytic center + (ii) The analytic center of the polytope + (iii) A boolean variable that declares convergence + + Note: Using MT as to deal with both dense and sparse matrices, MT_dense will be the type of result matrix +*/ +template +std::tuple analytic_center_linear_ineq(MT const& A, VT const& b, VT const& x0, + unsigned int const max_iters = 500, + NT const grad_err_tol = 1e-08, + NT const rel_pos_err_tol = 1e-12) +{ + // Initialization + VT x = x0; + VT Ax = A * x; + const int n = A.cols(), m = A.rows(); + MT H(n, n), A_trans = A.transpose(); + VT grad(n), d(n), Ad(m), b_Ax(m), step_d(n), x_prev; + NT grad_err, rel_pos_err, rel_pos_err_temp, step; + unsigned int iter = 0; + bool converged = false; + const NT tol_bnd = NT(0.01); + + auto llt = initialize_chol(A_trans, A); + get_hessian_grad_logbarrier(A, A_trans, b, x, Ax, H, grad, b_Ax); + + do { + iter++; + // Compute the direction + d.noalias() = - solve_vec(llt, H, grad); + Ad.noalias() = A * d; + // Compute the step length + step = std::min((NT(1) - tol_bnd) * get_max_step(Ad, b_Ax), NT(1)); + step_d.noalias() = step*d; + x_prev = x; + x += step_d; + Ax.noalias() += step*Ad; + + // Compute the max_i\{ |step*d_i| ./ |x_i| \} + rel_pos_err = std::numeric_limits::lowest(); + for (int i = 0; i < n; i++) + { + rel_pos_err_temp = std::abs(step_d.coeff(i) / x_prev.coeff(i)); + if (rel_pos_err_temp > rel_pos_err) + { + rel_pos_err = rel_pos_err_temp; + } + } + + get_hessian_grad_logbarrier(A, A_trans, b, x, Ax, H, grad, b_Ax); + grad_err = grad.norm(); + + if (iter >= max_iters || grad_err <= grad_err_tol || rel_pos_err <= rel_pos_err_tol) + { + converged = true; + break; + } + } while (true); + + return std::make_tuple(MT_dense(H), x, converged); +} + +template +std::tuple analytic_center_linear_ineq(MT const& A, VT const& b, + unsigned int const max_iters = 500, + NT const grad_err_tol = 1e-08, + NT const rel_pos_err_tol = 1e-12) +{ + VT x0 = compute_feasible_point(A, b); + return analytic_center_linear_ineq(A, b, x0, max_iters, grad_err_tol, rel_pos_err_tol); +} + +#endif diff --git a/include/random_walks/new_gaussian_accelerated_billiard_walk.hpp b/include/random_walks/new_gaussian_accelerated_billiard_walk.hpp new file mode 100644 index 000000000..80ea95ca9 --- /dev/null +++ b/include/random_walks/new_gaussian_accelerated_billiard_walk.hpp @@ -0,0 +1,277 @@ +// VolEsti (volume computation and sampling library) + +// Copyright (c) 2012-2020 Vissarion Fisikopoulos +// Copyright (c) 2018-2020 Apostolos Chalkis + +// Contributed and/or modified by Luca Perju, as part of Google Summer of Code 2024 program. + +// Licensed under GNU LGPL.3, see LICENCE file + +#ifndef RANDOM_WALKS_NEW_GAUSSIAN_ACCELERATED_BILLIARD_WALK_HPP +#define RANDOM_WALKS_NEW_GAUSSIAN_ACCELERATED_BILLIARD_WALK_HPP + +#include +#include +#include +#include "convex_bodies/orderpolytope.h" +#include "convex_bodies/ellipsoid.h" +#include "convex_bodies/ballintersectconvex.h" +#include "generators/boost_random_number_generator.hpp" +#include "sampling/ellipsoid.hpp" +#include "random_walks/uniform_billiard_walk.hpp" + +#include "random_walks/compute_diameter.hpp" + + +struct GABW +{ + GABW(double L) + : param(L, true) + {} + + GABW() + : param(0, false) + {} + + struct parameters + { + parameters(double L, bool set) + : m_L(L), set_L(set) + {} + double m_L; + bool set_L; + }; + + struct update_parameters + { + update_parameters() + : facet_prev(0), hit_ball(false), inner_vi_ak(0.0), ball_inner_norm(0.0) + {} + int facet_prev; + bool hit_ball; + double inner_vi_ak; + double ball_inner_norm; + }; + + parameters param; + + + template + < + typename Polytope, + typename RandomNumberGenerator + > + struct Walk + { + typedef typename Polytope::PointType Point; + typedef typename Polytope::MT MT; + typedef typename Polytope::VT VT; + typedef typename Point::FT NT; + + template + Walk(GenericPolytope& P, + Point const& p, + MT const& E, // covariance matrix representing the Gaussian distribution + RandomNumberGenerator &rng) + { + _update_parameters = update_parameters(); + _Len = compute_diameter + ::template compute(P); + + _AA.noalias() = P.get_mat() * P.get_mat().transpose(); + _rho = 1000 * P.dimension(); // upper bound for the number of reflections (experimental) + initialize(P, p, E, rng); + } + + template + Walk(GenericPolytope& P, + Point const& p, + MT const& E, // covariance matrix representing the Gaussian distribution + RandomNumberGenerator &rng, + parameters const& params) + { + _update_parameters = update_parameters(); + _Len = params.set_L ? params.m_L + : compute_diameter + ::template compute(P); + + _AA.noalias() = P.get_mat() * P.get_mat().transpose(); + _rho = 1000 * P.dimension(); // upper bound for the number of reflections (experimental) + initialize(P, p, E, rng); + } + + template + inline void apply(GenericPolytope& P, + Point &p, // a point to return the result + MT const& E, // covariance matrix representing the Gaussian distribution + unsigned int const& walk_length, + RandomNumberGenerator &rng) + { + unsigned int n = P.dimension(); + NT T; + const NT dl = 0.995; + int it; + NT coef = 1.0; + NT vEv; + + for (auto j=0u; j lltOfE(E.inverse()); // compute the Cholesky decomposition of inv(E) + if (lltOfE.info() != Eigen::Success) { + throw std::runtime_error("Cholesky decomposition failed!"); + } + _L_cov = lltOfE.matrixL(); + _v = GetDirection::apply(n, rng, false); + _v = Point(_L_cov.template triangularView() * _v.getCoefficients()); + coef = 1.0; + + vEv = (_v.getCoefficients().transpose() * E.template selfadjointView()).dot(_v.getCoefficients()); + + Point p0 = _p; + + it = 0; + std::pair pbpair; + if(!was_reset) + pbpair = P.line_positive_intersect(_p, _v, _lambdas, _Av, + _lambda_prev, _update_parameters); + else { + pbpair = P.line_first_positive_intersect(_p, _v, _lambdas, + _Av, _update_parameters); + was_reset = false; + } + + if (T <= pbpair.first) { + _p += (T * _v); + _lambda_prev = T; + continue; + } + + _lambda_prev = dl * pbpair.first; + _p += (_lambda_prev * _v); + T -= _lambda_prev; + coef = P.compute_reflection(_v, _p, _AE, _AEA, vEv, _update_parameters); + it++; + + + while (it < _rho) + { + std::pair pbpair + = P.line_positive_intersect(_p, _v, _lambdas, _Av, _lambda_prev, _AA, _update_parameters); + _Av *= coef; + _update_parameters.inner_vi_ak *= coef; + pbpair.first /= coef; + + if (T <= pbpair.first) { + _p += (T * _v); + _lambda_prev = T; + break; + } + _lambda_prev = dl * pbpair.first; + _p += (_lambda_prev * _v); + T -= _lambda_prev; + coef = P.compute_reflection(_v, _p, _AE, _AEA, vEv, _update_parameters); + it++; + } + if (it == _rho){ + _p = p0; + was_reset = true; + } + } + + p = _p; + } + + private : + + template + inline void initialize(GenericPolytope& P, + Point const& p, // a point to start + MT const& E, // covariance matrix representing the Gaussian distribution + RandomNumberGenerator &rng) + { + unsigned int n = P.dimension(); + const NT dl = 0.995; + was_reset = false; + _lambdas.setZero(P.num_of_hyperplanes()); + _Av.setZero(P.num_of_hyperplanes()); + _p = p; + _AE.noalias() = P.get_mat() * E; + _AEA.resize(P.num_of_hyperplanes()); + for(int i = 0; i < P.num_of_hyperplanes(); ++i) + { + _AEA(i) = _AE.row(i).dot(P.get_mat().row(i)); + } + + Eigen::LLT lltOfE(E.inverse()); // compute the Cholesky decomposition of inv(E) + if (lltOfE.info() != Eigen::Success) { + throw std::runtime_error("Cholesky decomposition failed!"); + } + _L_cov = lltOfE.matrixL(); + _v = GetDirection::apply(n, rng, false); + _v = Point(_L_cov.template triangularView() * _v.getCoefficients()); + + NT T = -std::log(rng.sample_urdist()) * _Len; + Point p0 = _p; + int it = 0; + NT coef = 1.0; + NT vEv = (_v.getCoefficients().transpose() * E.template selfadjointView()).dot(_v.getCoefficients()); + + std::pair pbpair + = P.line_first_positive_intersect(_p, _v, _lambdas, _Av, _update_parameters); + if (T <= pbpair.first) { + _p += (T * _v); + _lambda_prev = T; + return; + } + _lambda_prev = dl * pbpair.first; + _p += (_lambda_prev * _v); + T -= _lambda_prev; + coef = P.compute_reflection(_v, _p, _AE, _AEA, vEv, _update_parameters); + + while (it <= _rho) + { + std::pair pbpair + = P.line_positive_intersect(_p, _v, _lambdas, _Av, _lambda_prev, _AA, _update_parameters); + _Av *= coef; + _update_parameters.inner_vi_ak *= coef; + pbpair.first /= coef; + + if (T <= pbpair.first) { + _p += (T * _v); + _lambda_prev = T; + break; + } else if (it == _rho) { + _lambda_prev = rng.sample_urdist() * pbpair.first; + _p += (_lambda_prev * _v); + break; + } + _lambda_prev = dl * pbpair.first; + _p += (_lambda_prev * _v); + T -= _lambda_prev; + coef = P.compute_reflection(_v, _p, _AE, _AEA, vEv, _update_parameters); + it++; + } + } + + NT _Len; + Point _p; + Point _v; + NT _lambda_prev; + MT _AA; + MT _L_cov; // LL' = inv(E) + MT _AE; + VT _AEA; + unsigned int _rho; + update_parameters _update_parameters; + typename Point::Coeff _lambdas; + typename Point::Coeff _Av; + bool was_reset; + }; + +}; + + +#endif diff --git a/include/random_walks/random_walks.hpp b/include/random_walks/random_walks.hpp index 6c36457c1..aa154e4a1 100644 --- a/include/random_walks/random_walks.hpp +++ b/include/random_walks/random_walks.hpp @@ -24,6 +24,7 @@ #include "random_walks/uniform_vaidya_walk.hpp" #include "random_walks/uniform_accelerated_billiard_walk.hpp" #include "random_walks/gaussian_accelerated_billiard_walk.hpp" +#include "random_walks/new_gaussian_accelerated_billiard_walk.hpp" #include "random_walks/gaussian_hamiltonian_monte_carlo_exact_walk.hpp" #include "random_walks/exponential_hamiltonian_monte_carlo_exact_walk.hpp" #include "random_walks/uniform_accelerated_billiard_walk_parallel.hpp" diff --git a/include/random_walks/uniform_accelerated_billiard_walk.hpp b/include/random_walks/uniform_accelerated_billiard_walk.hpp index 980c8e5da..32c36ae19 100644 --- a/include/random_walks/uniform_accelerated_billiard_walk.hpp +++ b/include/random_walks/uniform_accelerated_billiard_walk.hpp @@ -104,8 +104,16 @@ struct AcceleratedBilliardWalk Point p0 = _p; it = 0; - std::pair pbpair = P.line_positive_intersect(_p, _v, _lambdas, _Av, + std::pair pbpair; + if(!was_reset) + pbpair = P.line_positive_intersect(_p, _v, _lambdas, _Av, _lambda_prev, _update_parameters); + else { + pbpair = P.line_first_positive_intersect(_p, _v, _lambdas, + _Av, _update_parameters); + was_reset = false; + } + if (T <= pbpair.first) { _p += (T * _v); _lambda_prev = T; @@ -134,7 +142,10 @@ struct AcceleratedBilliardWalk P.compute_reflection(_v, _p, _update_parameters); it++; } - if (it == _rho) _p = p0; + if (it == _rho) { + _p = p0; + was_reset = true; + } } p = _p; } @@ -231,6 +242,7 @@ struct AcceleratedBilliardWalk { unsigned int n = P.dimension(); const NT dl = 0.995; + was_reset = false; _lambdas.setZero(P.num_of_hyperplanes()); _Av.setZero(P.num_of_hyperplanes()); _p = p; @@ -301,6 +313,7 @@ struct AcceleratedBilliardWalk update_parameters _update_parameters; typename Point::Coeff _lambdas; typename Point::Coeff _Av; + bool was_reset; }; }; diff --git a/include/sampling/random_point_generators.hpp b/include/sampling/random_point_generators.hpp index e70e14617..a1647335d 100644 --- a/include/sampling/random_point_generators.hpp +++ b/include/sampling/random_point_generators.hpp @@ -392,4 +392,4 @@ struct ExponentialRandomPointGenerator -#endif // SAMPLERS_RANDOM_POINT_GENERATORS_HPP +#endif // SAMPLERS_RANDOM_POINT_GENERATORS_HPP \ No newline at end of file diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index 3adbe4681..37316dbb8 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -214,15 +214,16 @@ else () add_test(NAME test_geweke COMMAND mcmc_diagnostics_test -tc=geweke) add_test(NAME test_raftery COMMAND mcmc_diagnostics_test -tc=raftery) - add_executable (sampling_test sampling_test.cpp $) - add_test(NAME test_dikin COMMAND sampling_test -tc=dikin) - add_test(NAME test_john COMMAND sampling_test -tc=john) - add_test(NAME test_vaidya COMMAND sampling_test -tc=vaidya) - add_test(NAME test_brdhr COMMAND sampling_test -tc=brdhr) - add_test(NAME test_bcdhr COMMAND sampling_test -tc=bcdhr) - add_test(NAME test_grdhr COMMAND sampling_test -tc=grdhr) - add_test(NAME test_gbaw COMMAND sampling_test -tc=gbaw) - add_test(NAME test_ghmc COMMAND sampling_test -tc=ghmc) +add_executable (sampling_test sampling_test.cpp $) +add_test(NAME test_dikin COMMAND sampling_test -tc=dikin) +add_test(NAME test_john COMMAND sampling_test -tc=john) +add_test(NAME test_vaidya COMMAND sampling_test -tc=vaidya) +add_test(NAME test_brdhr COMMAND sampling_test -tc=brdhr) +add_test(NAME test_bcdhr COMMAND sampling_test -tc=bcdhr) +add_test(NAME test_grdhr COMMAND sampling_test -tc=grdhr) +add_test(NAME test_gbaw COMMAND sampling_test -tc=gbaw) +add_test(NAME test_ghmc COMMAND sampling_test -tc=ghmc) +add_test(NAME test_gabw COMMAND sampling_test -tc=gabw) add_executable (mmcs_test mmcs_test.cpp $) add_test(NAME test_mmcs COMMAND mmcs_test -tc=mmcs) diff --git a/test/new_rounding_test.cpp b/test/new_rounding_test.cpp index b3f6a2adf..bef83390a 100644 --- a/test/new_rounding_test.cpp +++ b/test/new_rounding_test.cpp @@ -211,4 +211,3 @@ TEST_CASE("round_max_ellipsoid") { TEST_CASE("round_svd") { call_test_svd(); } - diff --git a/test/sampling_test.cpp b/test/sampling_test.cpp index 3831e1ea4..b73878aa5 100644 --- a/test/sampling_test.cpp +++ b/test/sampling_test.cpp @@ -25,6 +25,8 @@ #include "diagnostics/univariate_psrf.hpp" +#include "preprocess/inscribed_ellipsoid_rounding.hpp" + template < @@ -299,6 +301,53 @@ void call_test_ghmc(){ CHECK(score.maxCoeff() < 2.2); } +template +void call_test_gabw(){ + typedef Cartesian Kernel; + typedef typename Kernel::Point Point; + typedef HPolytope Hpolytope; + typedef Eigen::Matrix MT; + typedef Eigen::Matrix VT; + Hpolytope P; + unsigned int d = 10; + typedef BoostRandomNumberGenerator RNGType; + + unsigned int walkL = 10, numpoints = 10000, nburns = 0; + RNGType rng(d); + Point StartingPoint(d); + std::list randPoints; + + std::cout << "--- Testing Gaussian Accelerated Billiard Walk for Skinny-H-cube10" << std::endl; + P = generate_skinny_cube(10); + + + Point p = P.ComputeInnerBall().first; + typedef typename GABW::template Walk + < + Hpolytope, + RNGType + > walk; + typedef MultivariateGaussianRandomPointGenerator RandomPointGenerator; + PushBackWalkPolicy push_back_policy; + + std::tuple ellipsoid = compute_inscribed_ellipsoid + (P.get_mat(), P.get_vec(), p.getCoefficients(), 500, std::pow(10, -6.0), std::pow(10, -4.0)); + const MT E = get<0>(ellipsoid); + + RandomPointGenerator::apply(P, p, E, numpoints, 1, randPoints, + push_back_policy, rng); + + MT samples(d, numpoints); + unsigned int jj = 0; + for (typename std::list::iterator rpit = randPoints.begin(); rpit!=randPoints.end(); rpit++, jj++) + samples.col(jj) = (*rpit).getCoefficients(); + + VT score = univariate_psrf(samples); + std::cout << "psrf = " << score.maxCoeff() << std::endl; + + CHECK(score.maxCoeff() < 1.1); +} + TEST_CASE("dikin") { call_test_dikin(); } @@ -330,3 +379,7 @@ TEST_CASE("gbaw") { TEST_CASE("ghmc") { call_test_ghmc(); } + +TEST_CASE("gabw") { + call_test_gabw(); +} From 9962387cac785f1ecd32db3ed450a8d79443882b Mon Sep 17 00:00:00 2001 From: lucaperju Date: Thu, 11 Jul 2024 14:46:14 +0200 Subject: [PATCH 32/35] minor changes for PR --- examples/crhmc_sampling/simple_crhmc.cpp | 2 +- .../preprocess/analytic_center_linear_ineq.h | 134 ------------------ include/sampling/random_point_generators.hpp | 2 +- test/new_rounding_test.cpp | 1 + 4 files changed, 3 insertions(+), 136 deletions(-) delete mode 100644 include/preprocess/analytic_center_linear_ineq.h diff --git a/examples/crhmc_sampling/simple_crhmc.cpp b/examples/crhmc_sampling/simple_crhmc.cpp index 8f07065b0..0a68dbc33 100644 --- a/examples/crhmc_sampling/simple_crhmc.cpp +++ b/examples/crhmc_sampling/simple_crhmc.cpp @@ -91,4 +91,4 @@ int main(int argc, char *argv[]) { run_main<16>(atoi(argv[2]), atoi(argv[3]), atoi(argv[4])); } return 0; -} +} \ No newline at end of file diff --git a/include/preprocess/analytic_center_linear_ineq.h b/include/preprocess/analytic_center_linear_ineq.h deleted file mode 100644 index 4a4852ac0..000000000 --- a/include/preprocess/analytic_center_linear_ineq.h +++ /dev/null @@ -1,134 +0,0 @@ -// VolEsti (volume computation and sampling library) - -// Copyright (c) 2024 Vissarion Fisikopoulos -// Copyright (c) 2024 Apostolos Chalkis -// Copyright (c) 2024 Elias Tsigaridas - -// Licensed under GNU LGPL.3, see LICENCE file - - -#ifndef ANALYTIC_CENTER_LINEAR_INEQ_H -#define ANALYTIC_CENTER_LINEAR_INEQ_H - -#include - -#include "preprocess/max_inscribed_ball.hpp" -#include "preprocess/feasible_point.hpp" -#include "preprocess/mat_computational_operators.h" - -template -NT get_max_step(VT const& Ad, VT const& b_Ax) -{ - const int m = Ad.size(); - NT max_element = std::numeric_limits::lowest(), max_element_temp; - for (int i = 0; i < m; i++) { - max_element_temp = Ad.coeff(i) / b_Ax.coeff(i); - if (max_element_temp > max_element) { - max_element = max_element_temp; - } - } - - return NT(1) / max_element; -} - -template -void get_hessian_grad_logbarrier(MT const& A, MT const& A_trans, VT const& b, - VT const& x, VT const& Ax, MT &H, VT &grad, VT &b_Ax) -{ - b_Ax.noalias() = b - Ax; - VT s = b_Ax.cwiseInverse(); - VT s_sq = s.cwiseProduct(s); - // Gradient of the log-barrier function - grad.noalias() = A_trans * s; - // Hessian of the log-barrier function - update_Atrans_Diag_A(H, A_trans, A, s_sq.asDiagonal()); -} - -/* - This implementation computes the analytic center of a polytope given - as a set of linear inequalities P = {x | Ax <= b}. The analytic center - is the minimizer of the log barrier function i.e., the optimal solution - of the following optimization problem (Convex Optimization, Boyd and Vandenberghe, Section 8.5.3), - - \min -\sum \log(b_i - a_i^Tx), where a_i is the i-th row of A. - - The function solves the problem by using the Newton method. - - Input: (i) Matrix A, vector b such that the polytope P = {x | Ax<=b} - (ii) The number of maximum iterations, max_iters - (iii) Tolerance parameter grad_err_tol to bound the L2-norm of the gradient - (iv) Tolerance parameter rel_pos_err_tol to check the relative progress in each iteration - - Output: (i) The Hessian computed on the analytic center - (ii) The analytic center of the polytope - (iii) A boolean variable that declares convergence - - Note: Using MT as to deal with both dense and sparse matrices, MT_dense will be the type of result matrix -*/ -template -std::tuple analytic_center_linear_ineq(MT const& A, VT const& b, VT const& x0, - unsigned int const max_iters = 500, - NT const grad_err_tol = 1e-08, - NT const rel_pos_err_tol = 1e-12) -{ - // Initialization - VT x = x0; - VT Ax = A * x; - const int n = A.cols(), m = A.rows(); - MT H(n, n), A_trans = A.transpose(); - VT grad(n), d(n), Ad(m), b_Ax(m), step_d(n), x_prev; - NT grad_err, rel_pos_err, rel_pos_err_temp, step; - unsigned int iter = 0; - bool converged = false; - const NT tol_bnd = NT(0.01); - - auto llt = initialize_chol(A_trans, A); - get_hessian_grad_logbarrier(A, A_trans, b, x, Ax, H, grad, b_Ax); - - do { - iter++; - // Compute the direction - d.noalias() = - solve_vec(llt, H, grad); - Ad.noalias() = A * d; - // Compute the step length - step = std::min((NT(1) - tol_bnd) * get_max_step(Ad, b_Ax), NT(1)); - step_d.noalias() = step*d; - x_prev = x; - x += step_d; - Ax.noalias() += step*Ad; - - // Compute the max_i\{ |step*d_i| ./ |x_i| \} - rel_pos_err = std::numeric_limits::lowest(); - for (int i = 0; i < n; i++) - { - rel_pos_err_temp = std::abs(step_d.coeff(i) / x_prev.coeff(i)); - if (rel_pos_err_temp > rel_pos_err) - { - rel_pos_err = rel_pos_err_temp; - } - } - - get_hessian_grad_logbarrier(A, A_trans, b, x, Ax, H, grad, b_Ax); - grad_err = grad.norm(); - - if (iter >= max_iters || grad_err <= grad_err_tol || rel_pos_err <= rel_pos_err_tol) - { - converged = true; - break; - } - } while (true); - - return std::make_tuple(MT_dense(H), x, converged); -} - -template -std::tuple analytic_center_linear_ineq(MT const& A, VT const& b, - unsigned int const max_iters = 500, - NT const grad_err_tol = 1e-08, - NT const rel_pos_err_tol = 1e-12) -{ - VT x0 = compute_feasible_point(A, b); - return analytic_center_linear_ineq(A, b, x0, max_iters, grad_err_tol, rel_pos_err_tol); -} - -#endif diff --git a/include/sampling/random_point_generators.hpp b/include/sampling/random_point_generators.hpp index a1647335d..e70e14617 100644 --- a/include/sampling/random_point_generators.hpp +++ b/include/sampling/random_point_generators.hpp @@ -392,4 +392,4 @@ struct ExponentialRandomPointGenerator -#endif // SAMPLERS_RANDOM_POINT_GENERATORS_HPP \ No newline at end of file +#endif // SAMPLERS_RANDOM_POINT_GENERATORS_HPP diff --git a/test/new_rounding_test.cpp b/test/new_rounding_test.cpp index bef83390a..b3f6a2adf 100644 --- a/test/new_rounding_test.cpp +++ b/test/new_rounding_test.cpp @@ -211,3 +211,4 @@ TEST_CASE("round_max_ellipsoid") { TEST_CASE("round_svd") { call_test_svd(); } + From 020b6c3ae2da2bbd9256763896638a1e47ee9b3c Mon Sep 17 00:00:00 2001 From: lucaperju Date: Fri, 12 Jul 2024 09:51:35 +0200 Subject: [PATCH 33/35] add space --- examples/crhmc_sampling/simple_crhmc.cpp | 2 +- include/misc/poset.h | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/crhmc_sampling/simple_crhmc.cpp b/examples/crhmc_sampling/simple_crhmc.cpp index 0a68dbc33..8f07065b0 100644 --- a/examples/crhmc_sampling/simple_crhmc.cpp +++ b/examples/crhmc_sampling/simple_crhmc.cpp @@ -91,4 +91,4 @@ int main(int argc, char *argv[]) { run_main<16>(atoi(argv[2]), atoi(argv[3]), atoi(argv[4])); } return 0; -} \ No newline at end of file +} diff --git a/include/misc/poset.h b/include/misc/poset.h index 17af82306..69ba02ec7 100644 --- a/include/misc/poset.h +++ b/include/misc/poset.h @@ -5,7 +5,6 @@ // Copyright (c) 2020-2021 Vaibhav Thakkar // Contributed and/or modified by Vaibhav Thakkar, as part of Google Summer of Code 2021 program. -// Contributed and/or modified by Luca Perju, as part of Google Summer of Code 2024 program. // Licensed under GNU LGPL.3, see LICENCE file From d01c6102160e98cdbd32caefc4d379af1371a81d Mon Sep 17 00:00:00 2001 From: lucaperju Date: Mon, 15 Jul 2024 16:07:45 +0200 Subject: [PATCH 34/35] Fix indentation, simplify equation --- include/convex_bodies/hpolytope.h | 3 +- ...new_gaussian_accelerated_billiard_walk.hpp | 29 +++++++++---------- .../uniform_accelerated_billiard_walk.hpp | 18 +++++------- ...orm_accelerated_billiard_walk_parallel.hpp | 8 ++--- 4 files changed, 26 insertions(+), 32 deletions(-) diff --git a/include/convex_bodies/hpolytope.h b/include/convex_bodies/hpolytope.h index 35e37c48c..2b0f566ef 100644 --- a/include/convex_bodies/hpolytope.h +++ b/include/convex_bodies/hpolytope.h @@ -913,8 +913,7 @@ class HPolytope { Point a((-2.0 * params.inner_vi_ak) * A.row(params.facet_prev)); VT x = v.getCoefficients(); - NT new_vEv = vEv - (4.0 * params.inner_vi_ak) * (AE.row(params.facet_prev).dot(x)) - + (4.0 * params.inner_vi_ak * params.inner_vi_ak) * AEA(params.facet_prev); + NT new_vEv = vEv - (4.0 * params.inner_vi_ak) * (AE.row(params.facet_prev).dot(x) - params.inner_vi_ak * AEA(params.facet_prev)); v += a; NT coeff = std::sqrt(vEv / new_vEv); v = v * coeff; diff --git a/include/random_walks/new_gaussian_accelerated_billiard_walk.hpp b/include/random_walks/new_gaussian_accelerated_billiard_walk.hpp index 80ea95ca9..74c8e6a68 100644 --- a/include/random_walks/new_gaussian_accelerated_billiard_walk.hpp +++ b/include/random_walks/new_gaussian_accelerated_billiard_walk.hpp @@ -57,10 +57,10 @@ struct GABW template - < - typename Polytope, - typename RandomNumberGenerator - > + < + typename Polytope, + typename RandomNumberGenerator + > struct Walk { typedef typename Polytope::PointType Point; @@ -75,8 +75,7 @@ struct GABW RandomNumberGenerator &rng) { _update_parameters = update_parameters(); - _Len = compute_diameter - ::template compute(P); + _L = compute_diameter::template compute(P); _AA.noalias() = P.get_mat() * P.get_mat().transpose(); _rho = 1000 * P.dimension(); // upper bound for the number of reflections (experimental) @@ -91,7 +90,7 @@ struct GABW parameters const& params) { _update_parameters = update_parameters(); - _Len = params.set_L ? params.m_L + _L = params.set_L ? params.m_L : compute_diameter ::template compute(P); @@ -116,7 +115,7 @@ struct GABW for (auto j=0u; j lltOfE(E.inverse()); // compute the Cholesky decomposition of inv(E) @@ -134,12 +133,10 @@ struct GABW it = 0; std::pair pbpair; - if(!was_reset) - pbpair = P.line_positive_intersect(_p, _v, _lambdas, _Av, - _lambda_prev, _update_parameters); - else { - pbpair = P.line_first_positive_intersect(_p, _v, _lambdas, - _Av, _update_parameters); + if(!was_reset) { + pbpair = P.line_positive_intersect(_p, _v, _lambdas, _Av, _lambda_prev, _update_parameters); + } else { + pbpair = P.line_first_positive_intersect(_p, _v, _lambdas, _Av, _update_parameters); was_reset = false; } @@ -213,7 +210,7 @@ struct GABW _v = GetDirection::apply(n, rng, false); _v = Point(_L_cov.template triangularView() * _v.getCoefficients()); - NT T = -std::log(rng.sample_urdist()) * _Len; + NT T = -std::log(rng.sample_urdist()) * _L; Point p0 = _p; int it = 0; NT coef = 1.0; @@ -256,7 +253,7 @@ struct GABW } } - NT _Len; + NT _L; Point _p; Point _v; NT _lambda_prev; diff --git a/include/random_walks/uniform_accelerated_billiard_walk.hpp b/include/random_walks/uniform_accelerated_billiard_walk.hpp index 32c36ae19..f5b6df767 100644 --- a/include/random_walks/uniform_accelerated_billiard_walk.hpp +++ b/include/random_walks/uniform_accelerated_billiard_walk.hpp @@ -49,10 +49,10 @@ struct AcceleratedBilliardWalk template - < - typename Polytope, - typename RandomNumberGenerator - > + < + typename Polytope, + typename RandomNumberGenerator + > struct Walk { typedef typename Polytope::PointType Point; @@ -105,12 +105,10 @@ struct AcceleratedBilliardWalk it = 0; std::pair pbpair; - if(!was_reset) - pbpair = P.line_positive_intersect(_p, _v, _lambdas, _Av, - _lambda_prev, _update_parameters); - else { - pbpair = P.line_first_positive_intersect(_p, _v, _lambdas, - _Av, _update_parameters); + if(!was_reset) { + pbpair = P.line_positive_intersect(_p, _v, _lambdas, _Av, _lambda_prev, _update_parameters); + } else { + pbpair = P.line_first_positive_intersect(_p, _v, _lambdas, _Av, _update_parameters); was_reset = false; } diff --git a/include/random_walks/uniform_accelerated_billiard_walk_parallel.hpp b/include/random_walks/uniform_accelerated_billiard_walk_parallel.hpp index 1c2630f9c..5250fb09d 100644 --- a/include/random_walks/uniform_accelerated_billiard_walk_parallel.hpp +++ b/include/random_walks/uniform_accelerated_billiard_walk_parallel.hpp @@ -53,10 +53,10 @@ struct AcceleratedBilliardWalkParallel template - < - typename Polytope, - typename RandomNumberGenerator - > + < + typename Polytope, + typename RandomNumberGenerator + > struct Walk { typedef typename Polytope::PointType Point; From fe0839b1e8f543fd6f1044463a6ae788ce4b70f1 Mon Sep 17 00:00:00 2001 From: lucaperju Date: Wed, 17 Jul 2024 10:10:40 +0200 Subject: [PATCH 35/35] minor optimizations --- ...new_gaussian_accelerated_billiard_walk.hpp | 46 +++++++++---------- include/sampling/random_point_generators.hpp | 2 +- 2 files changed, 22 insertions(+), 26 deletions(-) diff --git a/include/random_walks/new_gaussian_accelerated_billiard_walk.hpp b/include/random_walks/new_gaussian_accelerated_billiard_walk.hpp index 74c8e6a68..b3eed6f5d 100644 --- a/include/random_walks/new_gaussian_accelerated_billiard_walk.hpp +++ b/include/random_walks/new_gaussian_accelerated_billiard_walk.hpp @@ -12,7 +12,6 @@ #include #include -#include #include "convex_bodies/orderpolytope.h" #include "convex_bodies/ellipsoid.h" #include "convex_bodies/ballintersectconvex.h" @@ -76,10 +75,17 @@ struct GABW { _update_parameters = update_parameters(); _L = compute_diameter::template compute(P); + + Eigen::LLT lltOfE(E.llt().solve(MT::Identity(E.cols(), E.cols()))); // compute the Cholesky decomposition of inv(E) + if (lltOfE.info() != Eigen::Success) { + throw std::runtime_error("Cholesky decomposition failed!"); + } + _L_cov = lltOfE.matrixL(); + _E = E; _AA.noalias() = P.get_mat() * P.get_mat().transpose(); _rho = 1000 * P.dimension(); // upper bound for the number of reflections (experimental) - initialize(P, p, E, rng); + initialize(P, p, rng); } template @@ -94,15 +100,21 @@ struct GABW : compute_diameter ::template compute(P); + Eigen::LLT lltOfE(E.llt().solve(MT::Identity(E.cols(), E.cols()))); // compute the Cholesky decomposition of inv(E) + if (lltOfE.info() != Eigen::Success) { + throw std::runtime_error("Cholesky decomposition failed!"); + } + + _L_cov = lltOfE.matrixL(); + _E = E; _AA.noalias() = P.get_mat() * P.get_mat().transpose(); _rho = 1000 * P.dimension(); // upper bound for the number of reflections (experimental) - initialize(P, p, E, rng); + initialize(P, p, rng); } template inline void apply(GenericPolytope& P, Point &p, // a point to return the result - MT const& E, // covariance matrix representing the Gaussian distribution unsigned int const& walk_length, RandomNumberGenerator &rng) { @@ -117,17 +129,11 @@ struct GABW { T = -std::log(rng.sample_urdist()) * _L; - - Eigen::LLT lltOfE(E.inverse()); // compute the Cholesky decomposition of inv(E) - if (lltOfE.info() != Eigen::Success) { - throw std::runtime_error("Cholesky decomposition failed!"); - } - _L_cov = lltOfE.matrixL(); _v = GetDirection::apply(n, rng, false); _v = Point(_L_cov.template triangularView() * _v.getCoefficients()); coef = 1.0; - vEv = (_v.getCoefficients().transpose() * E.template selfadjointView()).dot(_v.getCoefficients()); + vEv = (_v.getCoefficients().transpose() * _E.template selfadjointView()).dot(_v.getCoefficients()); Point p0 = _p; @@ -152,7 +158,6 @@ struct GABW coef = P.compute_reflection(_v, _p, _AE, _AEA, vEv, _update_parameters); it++; - while (it < _rho) { std::pair pbpair @@ -186,7 +191,6 @@ struct GABW template inline void initialize(GenericPolytope& P, Point const& p, // a point to start - MT const& E, // covariance matrix representing the Gaussian distribution RandomNumberGenerator &rng) { unsigned int n = P.dimension(); @@ -195,18 +199,9 @@ struct GABW _lambdas.setZero(P.num_of_hyperplanes()); _Av.setZero(P.num_of_hyperplanes()); _p = p; - _AE.noalias() = P.get_mat() * E; - _AEA.resize(P.num_of_hyperplanes()); - for(int i = 0; i < P.num_of_hyperplanes(); ++i) - { - _AEA(i) = _AE.row(i).dot(P.get_mat().row(i)); - } + _AE.noalias() = P.get_mat() * _E; + _AEA = _AE.cwiseProduct(P.get_mat()).rowwise().sum(); - Eigen::LLT lltOfE(E.inverse()); // compute the Cholesky decomposition of inv(E) - if (lltOfE.info() != Eigen::Success) { - throw std::runtime_error("Cholesky decomposition failed!"); - } - _L_cov = lltOfE.matrixL(); _v = GetDirection::apply(n, rng, false); _v = Point(_L_cov.template triangularView() * _v.getCoefficients()); @@ -214,7 +209,7 @@ struct GABW Point p0 = _p; int it = 0; NT coef = 1.0; - NT vEv = (_v.getCoefficients().transpose() * E.template selfadjointView()).dot(_v.getCoefficients()); + NT vEv = (_v.getCoefficients().transpose() * _E.template selfadjointView()).dot(_v.getCoefficients()); std::pair pbpair = P.line_first_positive_intersect(_p, _v, _lambdas, _Av, _update_parameters); @@ -260,6 +255,7 @@ struct GABW MT _AA; MT _L_cov; // LL' = inv(E) MT _AE; + MT _E; VT _AEA; unsigned int _rho; update_parameters _update_parameters; diff --git a/include/sampling/random_point_generators.hpp b/include/sampling/random_point_generators.hpp index e70e14617..e38f50cac 100644 --- a/include/sampling/random_point_generators.hpp +++ b/include/sampling/random_point_generators.hpp @@ -121,7 +121,7 @@ struct MultivariateGaussianRandomPointGenerator Walk walk(P, p, E, rng); for (unsigned int i=0; i

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