{"ID":2893913,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.12112","arxiv_id":"2507.12112","title":"Convergence Rate of Generalized Nash Equilibrium Learning in Strongly Monotone Games with Linear Constraints","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.","short_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 convergenc...","url_abs":"https://arxiv.org/abs/2507.12112","url_pdf":"https://arxiv.org/pdf/2507.12112v1","authors":"[\"Tatiana Tatarenko\",\"Maryam Kamgarpour\"]","published":"2025-07-16T10:29:43Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}