Input-to-State Stability Framework for Fully Distributed Primal-Dual Dynamics for Quadratic GNEPs Without Multiplier Consensus
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Abstract (arXiv)
Generalized Nash Equilibrium Problems (GNEPs) often arise in multi-agent engineering applications that require distributed algorithms. Unlike traditional approaches that enforce consensus on multipliers, our method removes the need to share multipliers, reducing communication and improving privacy. As a result, different initializations can lead to different GNEs, including non-variational ones. We establish convergence under sufficient conditions using an input-to-state stability (ISS) framework.
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References · 9
- 2018Mean Field Multi-Agent Reinforcement Learningcited by 207
- 2021Global Convergence of Multi-Agent Policy Gradient in Markov Potential Gamescited by 93
- 2019ALGAMES: A Fast Solver for Constrained Dynamic Gamescited by 9
- 2021Convex-Concave Min-Max Stackelberg Gamescited by 9
- 2015Asymptotic convergence of constrained primal-dual dynamicscited by 8
- 2016Distributed Learning for Stochastic Generalized Nash Equilibrium Problemscited by 6
- 2019Continuous-time fully distributed generalized Nash equilibrium seeking for multi-integrator agentscited by 2
- 2020An asynchronous distributed and scalable generalized Nash equilibrium seeking algorithm for strongly monotone gamescited by 2
- 2026Fully Distributed GNE Algorithms for Multi-Robot Placement without Consensus on Multiplierscited by 1
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