Convergence Rate of Learning a Strongly Variationally Stable Equilibrium
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909133986332672 |
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| author | Tatarenko, Tatiana Kamgarpour, Maryam |
| author_facet | Tatarenko, Tatiana Kamgarpour, Maryam |
| contents | We derive the rate of convergence to the strongly variationally stable Nash equilibrium in a convex game, for a zeroth-order learning algorithm. Though we do not assume strong monotonicity of the game, our rates for the one-point feedback and for the two-point feedback match the best known rates for strongly monotone games under zeroth-order information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_02355 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Convergence Rate of Learning a Strongly Variationally Stable Equilibrium Tatarenko, Tatiana Kamgarpour, Maryam Optimization and Control We derive the rate of convergence to the strongly variationally stable Nash equilibrium in a convex game, for a zeroth-order learning algorithm. Though we do not assume strong monotonicity of the game, our rates for the one-point feedback and for the two-point feedback match the best known rates for strongly monotone games under zeroth-order information. |
| title | Convergence Rate of Learning a Strongly Variationally Stable Equilibrium |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2304.02355 |