# Learning Linear Complementarity Systems

@inproceedings{Jin2022LearningLC, title={Learning Linear Complementarity Systems}, author={Wanxin Jin and Alp Aydinoglu and Mathew Halm and Michael Posa}, booktitle={L4DC}, year={2022} }

This paper investigates the learning, or system identification, of a class of piecewiseaffine dynamical systems known as linear complementarity systems (LCSs). We propose a violation-based loss which enables efficient learning of the LCS parameterization, without prior knowledge of the hybrid mode boundaries, using gradient-based methods. The proposed violation-based loss incorporates both dynamics prediction loss and a novel complementarity violation loss. We show several properties attained…

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