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arXiv · 2105.04248

Feedback maximum principle for ensemble control of local continuity equations. An application to supervised machine learning

Maxim Staritsyn·Nikolay Pogodaev·Roman Chertovskih·Fernando Lobo Pereira
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Abstract

We consider an optimal control problem for a system of local continuity equations on a space of probability measures. Such systems can be viewed as macroscopic models of ensembles of non-interacting particles or homotypic individuals, representing several different ``populations''. For the stated problem, we propose a necessary optimality condition, which involves feedback controls inherent to the extremal structure, designed via the standard Pontryagin's Maximum Principle conditions. This optimality condition admits a realization as an iterative algorithm for optimal control. As a motivating case, we discuss an application of the derived optimality condition and the consequent numeric method to a problem of supervised machine learning via dynamic systems.

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Maxim Staritsyn, Nikolay Pogodaev, Roman Chertovskih, Fernando Lobo Pereira. 2021-05-10. Feedback maximum principle for ensemble control of local continuity equations. An application to supervised machine learning. https://doi.org/10.1109/lcsys.2021.3089139

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