Convergence Rate of Generalized Nash Equilibrium Learning in Strongly Monotone Games with Linear Constraints
Optimization and Control
2025-07-18 v1
Abstract
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.
Keywords
Cite
@article{arxiv.2507.12112,
title = {Convergence Rate of Generalized Nash Equilibrium Learning in Strongly Monotone Games with Linear Constraints},
author = {Tatiana Tatarenko and Maryam Kamgarpour},
journal= {arXiv preprint arXiv:2507.12112},
year = {2025}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2411.08595