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Input-to-State Stability Framework for Fully Distributed Primal-Dual Dynamics for Quadratic GNEPs Without Multiplier Consensus

arXiv 2609.06983 ↗First posted 7 Sep 2026math.OC cs.AI cs.GTcited by 0

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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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 26 references that are in the map. First posted on arXiv 7 Sep 2026.

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References · 9

  1. 2018Mean Field Multi-Agent Reinforcement Learningcited by 207
  2. 2021Global Convergence of Multi-Agent Policy Gradient in Markov Potential Gamescited by 93
  3. 2019ALGAMES: A Fast Solver for Constrained Dynamic Gamescited by 9
  4. 2021Convex-Concave Min-Max Stackelberg Gamescited by 9
  5. 2015Asymptotic convergence of constrained primal-dual dynamicscited by 8
  6. 2016Distributed Learning for Stochastic Generalized Nash Equilibrium Problemscited by 6
  7. 2019Continuous-time fully distributed generalized Nash equilibrium seeking for multi-integrator agentscited by 2
  8. 2020An asynchronous distributed and scalable generalized Nash equilibrium seeking algorithm for strongly monotone gamescited by 2
  9. 2026Fully Distributed GNE Algorithms for Multi-Robot Placement without Consensus on Multiplierscited by 1

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