Distributed Constrained Convex Optimization with Accumulated Subgradient Information over Undirected Switching Networks

Yuichi KAJIYAMA  Naoki HAYASHI  Shigemasa TAKAI  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E102-A   No.2   pp.343-350
Publication Date: 2019/02/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E102.A.343
Type of Manuscript: Special Section PAPER (Special Section on Mathematical Systems Science and its Applications)
convex optimization,  multi-agent systems,  distributed subgradient method,  

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This paper proposes a consensus-based subgradient method under a common constraint set with switching undirected graphs. In the proposed method, each agent has a state and an auxiliary variable as the estimates of an optimal solution and accumulated information of past gradients of neighbor agents. We show that the states of all agents asymptotically converge to one of the optimal solutions of the convex optimization problem. The simulation results show that the proposed consensus-based algorithm with accumulated subgradient information achieves faster convergence than the standard subgradient algorithm.