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arXiv · 2509.24157

Learning Hybrid Dynamics via Convex Optimizations

Kaito Iwasaki·Sangli Teng·Anthony Bloch·Maani Ghaffari
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Abstract

This paper investigates the problem of identifying state-dependent switching systems, a class of hybrid dynamical systems that combine multiple linear or nonlinear modes. We propose two broad classes of switching systems: switching linear systems (SLSs) and switching polynomial systems (SPSs). We first formulate the joint estimation of the mode dynamics and switching rules as a mixed integer program. To solve its inherent scalability issue, we develop a hierarchy of convex relaxations and establish a bound and conditions under which these relaxations are tight. Building on these results, we propose a bilevel convex optimization framework that alternates between mode assignment and dynamics estimation, and we recover switching boundaries using margin-based polynomial classifiers. Numerical experiments on both linear and nonlinear oscillators demonstrate that the method accurately identifies mode dynamics and reconstructs switching surfaces from trajectory data. Our results provide a tractable optimization-based framework for switching system identification.

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Kaito Iwasaki, Sangli Teng, Anthony Bloch, Maani Ghaffari. 2025-09-29. Learning Hybrid Dynamics via Convex Optimizations. https://arxiv.org/abs/2509.24157

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