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Nash Equilibria in Games with Playerwise Concave Coupling Constraints: Existence and Computation

arXiv 2509.14032 ↗First posted 17 Sep 2025cs.GT cs.LG cs.MAcited by 0

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Abstract (arXiv)

We study the existence and computation of Nash equilibria in concave games where the players' admissible strategies are subject to shared coupling constraints. Under playerwise concavity of constraints, we prove existence of Nash equilibria. Our proof leverages topological fixed point theory and novel structural insights into the contractibility of feasible sets, and relaxes strong assumptions for existence in prior work. Having established existence, we address the question of whether in the presence of coupling constraints, playerwise independent learning dynamics have convergence guarantees. We address this positively for the class of potential games by designing a convergent algorithm. To account for the possibly nonconvex feasible region, we employ a log barrier regularized gradient ascent with adaptive stepsizes. Starting from an initial feasible strategy profile and under exact gradient feedback, the proposed method converges to an ε-approximate constrained Nash equilibrium within O(ε-3) iterations.

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References · 17

  1. 2014Convex Optimization: Algorithms and Complexitycited by 575
  2. 2019A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approachcited by 143
  3. 2018Optimistic mirror descent in saddle-point problems: Going the extra (gradient) milecited by 136
  4. 2021Global Convergence of Multi-Agent Policy Gradient in Markov Potential Gamescited by 93
  5. 2020The Complexity of Constrained Min-Max Optimizationcited by 82
  6. 2022On Last-Iterate Convergence Beyond Zero-Sum Gamescited by 31
  7. 2022A unified stochastic approximation framework for learning in gamescited by 23
  8. 2014Inapproximability of Nash Equilibriumcited by 17
  9. 2021Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutionscited by 14
  10. 2022Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learningcited by 11
  11. 2024Independent Learning in Constrained Markov Potential Gamescited by 10
  12. 2022First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problemscited by 9
5 more
  1. 2023Provably Learning Nash Policies in Constrained Markov Potential Gamescited by 7
  2. 2022The Computational Complexity of Multi-player Concave Games and Kakutani Fixed Pointscited by 6
  3. 2024Improved Hardness Results for Min-Max Optimization with Coupled Constraintscited by 5
  4. 2018Worst-case iteration bounds for log barrier methods on problems with nonconvex constraintscited by 2
  5. 2024Convergence Rate of Payoff-based Generalized Nash Equilibrium Learningcited by 1

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