This repository will compile tips and tricks for solving optimization tasks of all kinds, continuous or discrete, constrained or unconstrained, smooth or nonsmooth, also special cases and rarely talked-about problem cases, but some comparisons between solvers available in R packages will be included.
Linear equality constraints
If there are only linear equality constraints (such as sum(x) == 1 or A x = b) there is a 'trick' that will enable this problem to be solved without constraints at all.
Stereographic optimization
We will look at the stereographic projection and how it can help to solve optimization problems under the 'unit-length' constraint sum(x^2) == 1.
The Remez problem
The Remez problem is the task of finding or calculating the best polynomial approximation of a continuous function on a closed interval such that the maximum absolute distance between the polynomial and the function is minimized.|
Recommended optimization solvers
The Optimization Task View presents a long list of optimization solvers available in R packages. Here is a selection of modern and state-of-the-art solvers, taken from the task view, for different optimization problem classes.
The 'Historize' routine
The Historize routine will enable functions to keep track of inputs and values during a series of function calls. This can be helpful for debugging and for visualizing calls, e.g., in integration or optimization applications.
Compare Nelder-Mead solvers
There are several implementations of Nelder-Mead optimization solvers in R packages. We will compare them in terms of accuracy and run-time behavior.
Solving 'Minimax' optimization problems
Functions that are defined as the maximum of other functions are not smooth and cannot be optimized by most solvers. We will show how this can be converted into a smooth problem and be solved exactly.
Visualizing optimization solutions
In higher dimensions, visualize a function along a line between two near-optimal solutions, helping to decide which one is the true optimum.
The Bin Packing problem
Solve the Bin Packing Problem (BPP) as a 'Mixed-Integer Linear Programming Problem' and use this model to compare some MILP solvers available in packages on CRAN.
Solve optimization problems with 'higher-accuracy' precision
If floating-point precision is not enough, there is the possibility to get more accurate optimization results by using 'big' floating-point numbers.