hlefebvr / KAdaptabilitySolver

A numerical optimization package for solving k-adaptability problems

k-adaptability-solver

k-adaptability-solver is a numerical optimization package, written in C++, for solving K-adaptability counterparts of two-stage mixed-integer robust optimization problems, based on our paper K-Adaptability in Two-Stage Mixed-Integer Robust Optimization.

If you use this code in your research, please cite:

@article{sgw:19,
  title   = {K-adaptability in two-stage mixed-integer robust optimization},
  author  = {Subramanyam, Anirudh and Gounaris, Chrysanthos E and Wiesemann, Wolfram},
  journal = {Mathematical Programming Computation},
  year    = {2019}
}

Requirements

• Cmake 2.8 or higher
• CPLEX 12.6 or higher
• OS X or Linux OS

Installation on OS X and Linux

• Obtain a copy of the CPLEX software and license here
• Specify the CPLEX installation directory in CMakeLists.txt
• Enter the following commands on the terminal

   $ mkdir build
   $ cd build/
   $ cmake Unix Makefiles -DCMAKE_BUILD_TYPE=Release ..
   $ make
  

• You should find an executable kadaptability in the build/ directory

Note

• You may also compile in debug mode by changing Release to Debug in the above command.
• You may change the TIME_LIMIT, MEMORY_LIMIT and NUM_THREADS parameters in inc/Constants.h. If you do modify the defaults, please re-build for the changes to take effect.

Usage

The problem (variables, objective function, constraints, uncertainty set etc) is modeled in the abstract class KAdaptableInfo defined in problemInfo.hpp/cpp.

To solve a new problem, you must create a new class which will inherit from this abstract class. You must then override the following functions:

• makeVars
• makeUncSet
• makeConsX
• makeConsY
• create
• clone

As an example, have a look at problemInfo_knp.hpp/cpp. This file implements examples 4.2 and 4.3 from the above paper.

After you have implemented the above class, you can solve it by creating a KAdaptableSolver object and calling the function solve_KAdaptability(K, heuristic, x). The solution vector is returned in x, and a summary of the run will be written to standard output.

As an example, please follow the main function defined in src/test.cpp.