Distributed quasi-monotone subgradient algorithm for nonsmooth convex optimization over directed graphs

作     者：Liang, Shu Wang, Leyi Yin, George 

作者机构：Univ Sci & Technol Beijing Sch Automat & Elect Engn Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA Wayne State Univ Dept Math Detroit MI 48202 USA 

出 版 物：《AUTOMATICA》 (自动学)

年 卷 期：2019年第101卷

页      面：175-181页

核心收录：

学科分类：0711[理学-系统科学] 0808[工学-电气工程] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0701[理学-数学] 071101[理学-系统理论] 

基　　金：U.S. Army Research Office [W911NF-15-1-0218] National Natural Science Foundation of China Fundamental Research Funds for the China Central Universities of USTB [FRF-TP-17-088A1] 

主　　题：Distributed optimization Nonsmooth convex optimization Quasi-monotone subgradient algorithm Directed graph 

摘      要：Distributed optimization is of essential importance in networked systems. Most of the existing distributed algorithms either assume the information exchange over undirected graphs, or require that the underlying directed network topology provides a doubly stochastic weight matrix to the agents. In this brief paper, a distributed subgradient-based algorithm is proposed to solve nonsmooth convex optimization problems. The algorithm applies to directed graphs without using a doubly stochastic weight matrix. Moreover, the algorithm is a distributed generalization and improvement of the quasi-monotone subgradient algorithm. An O(1/root k) convergence rate is achieved. The effectiveness of our algorithm is also illustrated by a numerical example. (C) 2018 Elsevier Ltd. All rights reserved.