From Few-Shot Optimal Control to Few-Shot Learning
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909539759030272 |
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| author | Chertovskih, Roman Pogodaev, Nikolay Staritsyn, Maxim Aguiar, A. Pedro |
| author_facet | Chertovskih, Roman Pogodaev, Nikolay Staritsyn, Maxim Aguiar, A. Pedro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13298 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Few-Shot Optimal Control to Few-Shot Learning Chertovskih, Roman Pogodaev, Nikolay Staritsyn, Maxim Aguiar, A. Pedro Optimization and Control Numerical Analysis 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. |
| title | From Few-Shot Optimal Control to Few-Shot Learning |
| topic | Optimization and Control Numerical Analysis |
| url | https://arxiv.org/abs/2503.13298 |