From Few-Shot Optimal Control to Few-Shot Learning

Roman Chertovskih; Nikolay Pogodaev; Maxim Staritsyn; A. Pedro Aguiar

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arXiv · 2503.13298

From Few-Shot Optimal Control to Few-Shot Learning

Roman Chertovskih·Nikolay Pogodaev·Maxim Staritsyn·A. Pedro Aguiar

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Abstract

We present an approach to solving unconstrained nonlinear optimal control problems for a broad class of dynamical systems. This approach involves lifting the nonlinear problem to a linear ``super-problem'' on a dual Banach space, followed by a non-standard ``exact'' variational analysis, -- culminating in a descent method that achieves rapid convergence with minimal iterations. We investigate the applicability of this framework to mean-field control and discuss its perspectives for the analysis of information propagation in self-interacting neural networks.

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Roman Chertovskih, Nikolay Pogodaev, Maxim Staritsyn, A. Pedro Aguiar. 2025-03-17. From Few-Shot Optimal Control to Few-Shot Learning. https://arxiv.org/abs/2503.13298

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