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

arXiv: https://arxiv.org/abs/2609.06983 · HTML: https://mlsanity.com/paper/2609.06983/

## 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.

## Builds on (most cited first, 9 of 9)

- [Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.05438/) (2018)
- [Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games](https://mlsanity.com/paper/2106.01969/) (2021)
- [ALGAMES: A Fast Solver for Constrained Dynamic Games](https://mlsanity.com/paper/1910.09713/) (2019)
- [Convex-Concave Min-Max Stackelberg Games](https://mlsanity.com/paper/2110.05192/) (2021)
- [Asymptotic convergence of constrained primal-dual dynamics](https://mlsanity.com/paper/1510.02150/) (2015)
- [Distributed Learning for Stochastic Generalized Nash Equilibrium Problems](https://mlsanity.com/paper/1608.00039/) (2016)
- [Continuous-time fully distributed generalized Nash equilibrium seeking for multi-integrator agents](https://mlsanity.com/paper/1911.12266/) (2019)
- [An asynchronous distributed and scalable generalized Nash equilibrium seeking algorithm for strongly monotone games](https://mlsanity.com/paper/2005.03507/) (2020)
- [Fully Distributed GNE Algorithms for Multi-Robot Placement without Consensus on Multipliers](https://mlsanity.com/paper/2608.29388/) (2026)

## 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.