Modeling piecewise affine dynamics is incredibly difficult, especially when you have a lot of pieces! Here, we make some significant headway by recasting the problem as learning a linear complementarity problem (LCP), which can represent exponentially more pieces than the number of learned parameters. Furthermore, the LCP structure exposes hidden complementarity variables, which can be used to construct a twice-differentiable loss, despite the model’s non-smooth predictions.
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