Decentralized multi-agent optimization based on a penalty method. (9th December 2022)

• Record Type: Journal Article
• Authors: Konnov, I.V.
• Is Part Of: Optimization. Volume 71:Number 15(2022)
• Page Start: 4529
• Page End: 4553
• Publication Date: 2022-12-09
• Subjects: Convex optimization -- constrained multi-agent optimization -- decentralized penalty method -- descent splitting method -- decomposition -- feasibility problem; 65K05 -- 90C06 -- 90C25 -- 68M14 -- 68W15 -- 93A14; Mathematical optimization -- Periodicals; 519.7
• DOI: 10.1080/02331934.2021.1950151
• Languages: English
• ISSNs: 0233-1934

Abstract

Abstract : We propose a decentralized penalty method for general convex constrained multi-agent optimization problems. Each auxiliary penalized problem is solved approximately with a special parallel descent splitting method. The method can be implemented in a computational network where each agent sends information only to the nearest neighbours. Convergence of the method is established under rather weak assumptions. We also describe a specialization of the proposed approach to the feasibility problem.