/ConvertChanceConstraint-ccc

ConvertChanceConstraint (ccc): a Matlab toolbox for Chance-constrained Optimization

Primary LanguageMATLABGNU General Public License v3.0GPL-3.0

ConvertChanceConstraint (ccc)

ConvertChanceConstraint (ccc): a Matlab toolbox for Chance-constrained Optimization

Basic Info

ConvertChanceConstraint (ccc) is a Matlab toolbox built upon YALMIP. With ccc, users could formulate chance-constrained optimization problems in YALMIP syntax, then ccc converts it to formats that can be solved by YALMIP and compatiable solvers. More details can be found in docs.

  • Latest Release (Work in Progress)
  • Development Version (master branch)
  • Suggested Citation If you find ccc useful in your work, we kindly request that you cite the following paper
@article{geng2019data,
  title={Data-driven decision making in power systems with probabilistic guarantees: Theory and applications of chance-constrained optimization},
  author={Geng, Xinbo and Xie, Le},
  journal={Annual Reviews in Control},
  year={2019},
  publisher={Elsevier}
}

Installation

  1. Install Matlab, the latest version is suggested.
  2. Install YALMIP, please follow the instructions guide here， the latest version is suggested.
    • [MPT 3.0] will be installed together with YALMIP
    • If you have MPT installed, make sure that you delete the YALMIP distribution residing inside MPT and remove the old path definitions. Better, don’t install YALMIP manually but use MPTs toolbox manager.
    • Getting started with YALMIP
  3. Add the core functions to Matlab path
    • by manually adding the ConvertChanceConstraint-ccc or ConvertChanceConstraint-ccc/code/ folder to Matlab path (by clicking Home --> Set Path --> Add with subfolders --> choose ConvertChanceConstraint-ccc/code/.
    • by running the installation script:
  4. Test the installation by running the test code in

Bug reports

  • Please report any issues via the Github issue tracker.
  • All types of issues are welcome and encouraged, e.g. bug reports, documentation typos, feature requests, etc.
  • No guarantee on the compatiability with GNU Octave.

Documentation

  • An overview of methods to solve Chance-constrained Optimization
  • Core functions
  • Illustrative examples
  • Detailed documentation and its outline

ccc is designed for prototyping or medium-scale problems, it could be very slow when handling large-scale optimization problems.

More information can be found on the author's website.

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