Well-posedness and mean-field limit estimate of a consensus-based algorithm for multiplayer games
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915293354262528 |
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| author | Huang, Hui Warnett, Jethro |
| author_facet | Huang, Hui Warnett, Jethro |
| contents | Recently, the paper [12] introduces a derivative-free consensus-based particle method that finds the Nash equilibrium of non-convex multiplayer games, where it proves the global exponential convergence in the sense of mean-field law. This paper aims to address theoretical gaps in [12], specifically by providing a quantitative estimate of the mean-field limit with respect to the number of particles, as well as establishing the well-posedness of both the finite particle model and the corresponding mean-field dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13632 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Well-posedness and mean-field limit estimate of a consensus-based algorithm for multiplayer games Huang, Hui Warnett, Jethro Optimization and Control Classical Analysis and ODEs Probability Recently, the paper [12] introduces a derivative-free consensus-based particle method that finds the Nash equilibrium of non-convex multiplayer games, where it proves the global exponential convergence in the sense of mean-field law. This paper aims to address theoretical gaps in [12], specifically by providing a quantitative estimate of the mean-field limit with respect to the number of particles, as well as establishing the well-posedness of both the finite particle model and the corresponding mean-field dynamics. |
| title | Well-posedness and mean-field limit estimate of a consensus-based algorithm for multiplayer games |
| topic | Optimization and Control Classical Analysis and ODEs Probability |
| url | https://arxiv.org/abs/2505.13632 |