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

Year: 2026

Type: preprint

Abstract: Recent machine learning research has increasingly focused on equilibrium analysis in non-cooperative games rather than solely on optimal solutions. Many such problems involve shared constraints and can be formulated as Generalized Nash Equilibrium Problems (GNEPs). For strongly monotone games, existing methods compute consensus-based variational GNEs (v-GNEs) by exchanging Lagrange multipliers. We propose a fully distributed continuous-time algorithm for shared linear equality constraints that converges without multiplier exchange and reaches any GNE, reducing communication overhead and improving privacy. Discrete-time schemes are also provided, and the method is validated on a multi-robot placement task.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2608.29388

Authors: Yin, Shao-An, Hong, Mingyi, Elia, Nicola

Institution: University of Minnesota

Topic: Distributed Control Multi-Agent Systems

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Award: A Multi-Rate Feedback Control Framework for Design and Analyzing of Decentralized and Federated Learning

OpenAlex ID: W7204868293

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