arXiv AI September 10, 2026 By Shao-An Yin

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

The paper presents a distributed primal‑dual algorithm for quadratic Generalized Nash Equilibrium Problems (GNEPs) that eliminates the requirement for multiplier consensus. By removing shared multipliers, the method reduces communication overhead and enhances privacy, allowing different initializations to converge to distinct GNEs, including non‑variational equilibria. Convergence is proven under sufficient conditions using an input‑to‑state stability (ISS) framework.

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arXiv AI

Sep 1

Fully Distributed GNE Algorithms for Multi-Robot Placement without Consensus on Multipliers

The paper introduces a fully distributed continuous‑time algorithm for solving Generalized Nash Equilibrium Problems (GNEPs) with shared linear equality constraints. Unlike existing methods that require exchanging Lagrange multipliers, this approach converges to any GNE without multiplier communication, thereby reducing communication overhead and enhancing privacy. Discrete‑time variants are also presented and the method is demonstrated on a multi‑robot placement task.

By Shao-An Yin, Mingyi Hong, Nicola Elia

robotics safety

arXiv AI

Jul 23

Distributed Optimization via Energy Conservation Laws in Dilated Coordinates

arXiv:2409. 19279v2 Announce Type: replace-cross Abstract: Continuous-time models can reveal accelerated structures in distributed optimization, but their rates need not survive direct discretization.

By Kushal Chakrabarti, Mayank Baranwal

agents

arXiv Machine Learning

Jun 29

Second-Order KKT Guarantees for Bregman ADMM in Nonconvex and Non-Lipschitz Optimization

arXiv:2606. 28307v1 Announce Type: cross Abstract: We analyze Bregman ADMM for nonconvex linearly constrained problems under two-sided relative smoothness, a condition that replaces the standard Lipschitz gradient assumption with a Hessian comparison relative to a Bregman kernel.

By Shuang Li, Zhihui Zhu, Qiuwei Li

arXiv Statistics ML

Sep 15

Distributed Fast Fixed-Point Algorithms for Composite Monotone Inclusions over Networks

arXiv:2609.14953v1 Announce Type: cross Abstract: This paper aims to develop new and efficient distributed algorithms for solving a class of monotone inclusions, $0 \in \sum_{i=1}^n (G_ix + T_ix)$, o...

By Nghia Nguyen-Trung, Ion Necoara, Quoc Tran-Dinh

agents

arXiv Machine Learning

Sep 15

Equilibrium bias and convergence in augmented primal--dual dynamics with sampled constraints

arXiv:2609.13925v1 Announce Type: cross Abstract: This work studies the stability and convergence of augmented primal-dual dynamics when constraint values are estimated from samples. Unbiased constra...

By Kang Liu, Mengxiao Chen, Siqi Xiong, Yi Xia

safety

arXiv Machine Learning

Jun 2

Distributed GNEP Algorithms without Multiplier Sharing and Applications to Multi-Robot Coordination and Contextual Bandit-Based Active Learning

arXiv:2606. 00759v1 Announce Type: new Abstract: Recent advances in artificial intelligence have expanded the focus from classical optimization to include equilibrium analysis in noncooperative games.

By Shao-An Yin

agents reinforcement-learning robotics safety

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