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

2026-09-07

AI Summary

本文提出了一种无需共享乘子的全分布式主对偶动力学方法，利用输入到状态稳定性框架解决了二次广义纳什均衡问题。

Abstract

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.

Problem

• GNEPs
• distributed algorithms
• input-to-state stability
• multipliers
• convergence

Innovation

• Input-to-State Stability
• Distributed Primal-Dual Dynamics
• Quadratic GNEPs
• Multiplier Consensus