Merge pull request #167 from aadler/Patch-Fix-Test-Banana

Completely rewrite and greatly expand the banana tests.

inst/tinytest/test-Rosenbrock-banana.R

@@ -0,0 +1,303 @@
+# Copyright (C) 2024 Avraham Adler. All Rights Reserved.
+# SPDX-License-Identifier: LGPL-3.0-or-later
+#
+# File:   test-Rosebbrock-banana
+# Author: Avraham Adler
+# Date:   25 June 2024
+#
+# Complete rewrite of the current Rosenbrock banana tests. This also helps test
+# the accuracy of various algorithms.
+#
+# Changelog:
+#   2024-06-25: Complete rewrite of existing (inefficient) tests. Also tests
+#               most of the exposed algorithms. See the comments here and
+#               https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/ for
+#               more details.
+#
+
+library(nloptr)
+tol <- sqrt(.Machine$double.eps)
+
+# Rosenbrock banana function (rbf)
+rbf <- function(x) {(1 - x[1]) ^ 2 + 100 * (x[2] - x[1] ^ 2) ^ 2}
+
+# Analytic gradient for rbf
+rbfgr <- function(x) {c(-2 * (1 - x[1]) - 400 * x[1] * (x[2] - x[1] ^ 2),
+                     200 * (x[2] - x[1] ^ 2))}
+
+# Used options
+opts <- list(ftol_rel = 1e-12, xtol_rel = 1e-12, print_level = 0, maxeval = 5e4)
+
+# Known optimium of 0 occurs at (1, 1)
+rbfOptVal <- 0
+rbfOptLoc <- c(1, 1)
+
+# Initial values
+x0 <- c(-1.2, 1.3)
+
+# Local Gradient-Based Algorithms
+## LBFGS (also tests seperate and combined function/gradient calls).
+
+opts$algorithm <- "NLOPT_LD_LBFGS"
+
+# Test passing function and gradient separately.
+testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+# Test passing function and gradient in same function call.
+rbfComplete <- function(x) {
+  list(objective = rbf(x), gradient = rbfgr(x))
+}
+
+testRes <- nloptr(x0 = x0, eval_f = rbfComplete, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+## MMA
+opts$algorithm <- "NLOPT_LD_MMA"
+testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+## CCSAQ
+opts$algorithm <- "NLOPT_LD_CCSAQ"
+testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+## SLSQP
+opts$algorithm <- "NLOPT_LD_SLSQP"
+testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+## Preconditioned truncated Newton
+opts$algorithm <- "NLOPT_LD_TNEWTON"
+testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+opts$algorithm <- "NLOPT_LD_TNEWTON_RESTART"
+testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+opts$algorithm <- "NLOPT_LD_TNEWTON_PRECOND"
+testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+opts$algorithm <- "NLOPT_LD_TNEWTON_PRECOND_RESTART"
+testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+## Shifted limited-memory variable-metric
+opts$algorithm <- "NLOPT_LD_VAR2"
+testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = 1e-7)
+
+opts$algorithm <- "NLOPT_LD_VAR1"
+testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+# Local Derivative-Free Algorithms
+## COBYLA
+opts$algorithm <- "NLOPT_LN_COBYLA"
+testRes <- nloptr(x0 = x0, eval_f = rbf, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+## BOBYQA
+opts$algorithm <- "NLOPT_LN_BOBYQA"
+testRes <- nloptr(x0 = x0, eval_f = rbf, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+## NEWUOA
+opts$algorithm <- "NLOPT_LN_NEWUOA"
+testRes <- nloptr(x0 = x0, eval_f = rbf, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+## PRAXIS
+opts$algorithm <- "NLOPT_LN_PRAXIS"
+testRes <- nloptr(x0 = x0, eval_f = rbf, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+## Nelder-Mead Simplex
+opts$algorithm <- "NLOPT_LN_NELDERMEAD"
+testRes <- nloptr(x0 = x0, eval_f = rbf, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+## Sbplx
+opts$algorithm <- "NLOPT_LN_SBPLX"
+testRes <- nloptr(x0 = x0, eval_f = rbf, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+# Global Algorithms
+lb <- c(-3, -3)
+ub <- c(3,  3)
+
+## StoGo
+## StoGo passes on many platforms but fails MISERABLE (Inf???) on others. Note
+## that here and disable the tests for now.
+# opts$algorithm <- "NLOPT_GD_STOGO"
+# testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, lb = lb, ub = ub,
+#                   opts = opts)
+#
+# expect_equal(testRes$objective, rbfOptVal, tolerance = 1e-4)
+# expect_equal(testRes$solution, rbfOptLoc, tolerance = 1e-4)
+#
+# opts$algorithm <- "NLOPT_GD_STOGO_RAND"
+# testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, lb = lb, ub = ub,
+#                   opts = opts)
+#
+# expect_equal(testRes$objective, rbfOptVal, tolerance = 1e-4)
+# expect_equal(testRes$solution, rbfOptLoc, tolerance = 1e-4)
+
+## ISRES
+opts$population <- 100
+opts$ranseed <- 2718L
+opts$algorithm <- "NLOPT_GN_ISRES"
+
+testRes <- nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+## Controlled Random Search (with ranseed testing)
+opts$algorithm <- "NLOPT_GN_CRS2_LM"
+testRes <- nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+# Different random seed
+opts$ranseed <- 3141L
+testRes2 <- nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+# Same random seed
+opts$ranseed <- 2718L
+testRes3 <- nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+# Results of different random seeds should differ.
+expect_false(identical(testRes$objective, testRes2$objective))
+expect_false(identical(testRes$solution, testRes2$solution))
+
+# Results of same random seeds should be the same.
+expect_identical(testRes$objective, testRes3$objective)
+expect_identical(testRes$solution, testRes3$solution)
+
+## DIRECT
+opts$algorithm <- "NLOPT_GN_DIRECT_L"
+testRes <- nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts)
+
+# expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+# expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+opts$algorithm <- "NLOPT_GN_DIRECT_NOSCAL"
+testRes <- nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+opts$algorithm <- "NLOPT_GN_DIRECT_L_NOSCAL"
+testRes <- nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+opts$algorithm <- "NLOPT_GN_DIRECT_L_RAND_NOSCAL"
+testRes <- nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+opts$algorithm <- "NLOPT_GN_ORIG_DIRECT_L"
+testRes <- nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+# The follwing versions converge to the wrong answer; see
+# https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#direct-and-direct-l
+# in that the rescaling may be faulty for this particular problem.
+opts$algorithm <- "NLOPT_GN_DIRECT"
+expect_stdout(nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts))
+
+opts$algorithm <- "NLOPT_GN_ORIG_DIRECT"
+expect_stdout(nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts))
+
+## ESCH - does not converge in 1M iterations so just test for output. Probably
+## needs MUCH tighter bounds.
+opts$algorithm <- "NLOPT_GN_ESCH"
+expect_stdout(nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts))
+
+## MLSL (Multi-Level Single-Linkage)
+# Use LBGFS as local search algorithm
+opts$local_opts <- list(algorithm = "NLOPT_LD_LBFGS", xtol_rel = 1e-9)
+# Need to set lower evaluation cap since this is nested global/local
+oldmaxeval <- opts$maxeval
+opts$maxeval <- 1000
+
+# Gradient-based
+opts$algorithm <- "NLOPT_GD_MLSL"
+testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, lb = lb, ub = ub,
+                  opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+opts$algorithm <- "NLOPT_GD_MLSL_LDS"
+testRes <- nloptr(x0 = x0, eval_f = rbf, eval_grad_f = rbfgr, lb = lb, ub = ub,
+                  opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = tol)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = tol)
+
+# Derivative-free
+opts$local_opts <- list(algorithm = "NLOPT_LN_NELDERMEAD", xtol_rel = 1e-12)
+opts$maxeval <- 10000
+
+# Need lower tolerance (or MANY more evaluations) without gradient information.
+opts$algorithm <- "NLOPT_GN_MLSL"
+testRes <- nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = 1e-5)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = 1e-5)
+
+opts$algorithm <- "NLOPT_GN_MLSL_LDS"
+testRes <- nloptr(x0 = x0, eval_f = rbf, lb = lb, ub = ub, opts = opts)
+
+expect_equal(testRes$objective, rbfOptVal, tolerance = 1e-5)
+expect_equal(testRes$solution, rbfOptLoc, tolerance = 1e-5)