Convergence Rate of Generalized Nash Equilibrium Learning in Strongly Monotone Games with Linear Constraints
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913946585268224 |
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| author | Tatarenko, Tatiana Kamgarpour, Maryam |
| author_facet | Tatarenko, Tatiana Kamgarpour, Maryam |
| contents | We consider payoff-based learning of a generalized Nash equilibrium (GNE) in multi-agent systems. Our focus is on games with jointly convex constraints of a linear structure and strongly monotone pseudo-gradients. We present a convergent procedure based on a partial regularization technique and establish the convergence rate of its iterates under one- and two-point payoff-based feedback. To the best of our knowledge, this work is the first one characterizing the convergence speed of iterates to a variational GNE in the class of games under consideration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12112 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Convergence Rate of Generalized Nash Equilibrium Learning in Strongly Monotone Games with Linear Constraints Tatarenko, Tatiana Kamgarpour, Maryam Optimization and Control We consider payoff-based learning of a generalized Nash equilibrium (GNE) in multi-agent systems. Our focus is on games with jointly convex constraints of a linear structure and strongly monotone pseudo-gradients. We present a convergent procedure based on a partial regularization technique and establish the convergence rate of its iterates under one- and two-point payoff-based feedback. To the best of our knowledge, this work is the first one characterizing the convergence speed of iterates to a variational GNE in the class of games under consideration. |
| title | Convergence Rate of Generalized Nash Equilibrium Learning in Strongly Monotone Games with Linear Constraints |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2507.12112 |