Learning Linear Complementarity Systems

The code is an implementation of the paper Learning Linear Complementarity Systems, coauthored by Wanxin Jin, Alp Aydinoglu, Mathew Halm, and Michael Posa. We have developed a new learning formulation to learn a linear complementarity system (LCS), which consists of a linear dynamics model and complementarity constraint.

Overview

Folder lcs is a python package, which includes

• lcs_learning.py, which packs different utility classes/functions necessary for learning and testing a LCS, particularly including a class LCS_VN for learning a LCS by the proposed violation-based method and a class LCS_PN for learning a LCS by the prediction-based method. For technical details of those two different learning techniques, please refer to our paper.

• optim.py is an implementation of different gradient-based techniques.

Folder evaluations contains different evaluations of the proposed violation-based learning method (LCS_VN) in comparison with the prediction-based method (LCS_PN).

RUN: the quick way to start the code is to run each script in the evaluations folder. All notations/variable used in the code follow the paper or conventions.

Dependency

• CasADi: for solving QP and autodiff. (>= 3.5.1. Info: https://web.casadi.org/), be sure to install qpoases plugin.
• Numpy: for matrix computation. (>= 1.18.1. Info: https://numpy.org/)

Citation

If you find this project/paper helpful in your research, please consider citing our paper.

@misc{jin2021learning,
      title={Learning Linear Complementarity Systems}, 
      author={Wanxin Jin and Alp Aydinoglu and Mathew Halm and Michael Posa},
      year={2021},
      eprint={2112.13284},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}