On the coercivity condition in the learning of interacting particle systems

Zhongyang Li; Fei Lu

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arXiv · 2011.10480

On the coercivity condition in the learning of interacting particle systems

Zhongyang Li·Fei Lu

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Abstract

In the learning of systems of interacting particles or agents, coercivity condition ensures identifiability of the interaction functions, providing the foundation of learning by nonparametric regression. The coercivity condition is equivalent to the strictly positive definiteness of an integral kernel arising in the learning. We show that for a class of interaction functions such that the system is ergodic, the integral kernel is strictly positive definite, and hence the coercivity condition holds true.

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Zhongyang Li, Fei Lu. 2020-11-20. On the coercivity condition in the learning of interacting particle systems. https://arxiv.org/abs/2011.10480

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