Convergence Rate of Payoff-based Generalized Nash Equilibrium Learning

Autor:	Tatarenko, Tatiana, Kamgarpour, Maryam
Rok vydání:	2024
Předmět:	Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.08595 Zobrazit plný text záznamu View this record from Arxiv