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A numerical optimization package for solving k-adaptability problems

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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.

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A numerical optimization package for solving k-adaptability problems

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