Distributed gradient descent method with edge‐based event‐driven communication for non‐convex optimization. Issue 12 (22nd May 2021)

• Record Type: Journal Article
• Authors: Adachi, T.; Hayashi, N.; Takai, S.
• Is Part Of: IET control theory & applications. Volume 15:Issue 12(2021)
• Page Start: 1588
• Page End: 1598
• Publication Date: 2021-05-22
• Subjects: Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/cth2.12127
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract: This paper considers an event‐driven distributed non‐convex optimization algorithm for a multi‐agent system, where each agent has a non‐convex cost function. The goal of the multi‐agent system is to minimize the global objective function, which is the sum of these local cost functions, in a distributed manner. To this end, each agent updates the own state by a consensus‐based gradient descent algorithm. The local information exchange among neighbor agents is carried out with an event‐triggered scheme to achieve consensus with less inter‐agent communication. Convergence to a critical point of the objective function and the validity of the proposed algorithm in numerical examples are shown.