# Convergence Rate of Generalized Nash Equilibrium Learning in Strongly Monotone Games with Linear Constraints

arXiv: https://arxiv.org/abs/2507.12112 · HTML: https://mlsanity.com/paper/2507.12112/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [Last-Iterate Guarantees for Learning in Co-coercive Games](https://mlsanity.com/paper/2604.19065/) (2026)

## Abstract (arXiv)

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.