Convergence Rate of Payoff-based Generalized Nash Equilibrium Learning

Fuente: arXiv
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Main Authors: Tatarenko, Tatiana, Kamgarpour, Maryam
Format: Preprint
Published: 2024
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author Tatarenko, Tatiana
Kamgarpour, Maryam
author_facet Tatarenko, Tatiana
Kamgarpour, Maryam
contents We consider generalized Nash equilibrium (GNE) problems in games with strongly monotone pseudo-gradients and jointly linear coupling constraints. We establish the convergence rate of a payoff-based approach intended to learn a variational GNE (v-GNE) in such games. While convergent algorithms have recently been proposed in this setting given full or partial information of the gradients, rate of convergence in the payoff-based information setting has been an open problem. Leveraging properties of a game extended from the original one by a dual player, we establish a convergence rate of $O(\frac{1}{t^{4/7}})$ to a v-GNE of the game.
format Preprint
id arxiv_https___arxiv_org_abs_2411_08595
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Convergence Rate of Payoff-based Generalized Nash Equilibrium Learning
Tatarenko, Tatiana
Kamgarpour, Maryam
Optimization and Control
We consider generalized Nash equilibrium (GNE) problems in games with strongly monotone pseudo-gradients and jointly linear coupling constraints. We establish the convergence rate of a payoff-based approach intended to learn a variational GNE (v-GNE) in such games. While convergent algorithms have recently been proposed in this setting given full or partial information of the gradients, rate of convergence in the payoff-based information setting has been an open problem. Leveraging properties of a game extended from the original one by a dual player, we establish a convergence rate of $O(\frac{1}{t^{4/7}})$ to a v-GNE of the game.
title Convergence Rate of Payoff-based Generalized Nash Equilibrium Learning
topic Optimization and Control
url https://arxiv.org/abs/2411.08595