Nash Equilibria in Games with Playerwise Concave Coupling Constraints: Existence and Computation
Philip Jordan, Maryam Kamgarpour
Abstract
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 -approximate constrained Nash equilibrium within iterations.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Builds on4
- Global Convergence of Multi-Agent Policy Gradient in Markov Potential GamesStefanos Leonardos, Will Overman, Ioannis Panageas, Georgios PiliourasICLR 2022 · 158 citations
- On Last-Iterate Convergence Beyond Zero-Sum GamesIoannis Anagnostides, Ioannis Panageas, Gabriele Farina, Tuomas SandholmICML 2022 · 52 citations
- Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form SolutionsArda Sahiner, Tolga Ergen, Batu Ozturkler, Burak Bartan et al.ICLR 2022 · 23 citations
- The complexity of constrained min-max optimizationConstantinos Daskalakis, Stratis Skoulakis, Manolis ZampetakisSTOC 2021 · 18 citations
Related papers
- Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum GamesTanner Fiez, Lillian J. Ratliff, Eric Mazumdar, Evan Faulkner et al.NeurIPS 2021 · 29 citations
- Near-Optimal No-Regret Learning Dynamics for General Convex GamesGabriele Farina, Ioannis Anagnostides, Haipeng Luo, Chung-Wei Lee et al.NeurIPS 2022 · 43 citations
- Computing Nash Equilibria in Potential Games with Private Uncoupled ConstraintsNikolas Patris, Stelios Stavroulakis, Fivos Kalogiannis, Rose Zhang et al.AAAI 2024 · 1 citation
- Constrained Phi-EquilibriaMartino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Francesco Trovò et al.ICML 2023
- Uncoupled and Convergent Learning in Monotone Games under Bandit FeedbackJing Dong, Baoxiang Wang, Yaoliang YuNeurIPS 2025 · 6 citations
