Distributed consensus-based solver for semi-definite programming: An optimization viewpoint

被引：5
|
作者：
Li, Weijian [1 ]
Zeng, Xianlin [2 ]
Hong, Yiguang [3 ,4 ]
Ji, Haibo [1 ]
机构：
[1] Univ Sci & Technol China, Dept Automat, Hefei 230027, Anhui, Peoples R China
[2] Beijing Inst Technol, Sch Automat, Key Lab Intelligent Control & Decis Complex Syst, Beijing 100081, Peoples R China
[3] Tongji Univ, Dept Control Sci & Engn, Shanghai 201804, Peoples R China
[4] Shanghai Res Inst Intelligent Syst, Shanghai 201203, Peoples R China
来源：
AUTOMATICA | 2021年 / 131卷
基金：
中国国家自然科学基金;
关键词：
Distributed optimization; Consensus-based algorithm; Semi-definite programming; Sparsity; POINT METHOD; DYNAMICS; FLOW;
D O I：
10.1016/j.automatica.2021.109737
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper aims at the distributed computation for semi-definite programming (SDP) problems over multi-agent networks. Two SDP problems, including a non-sparse case and a sparse case, are transformed into distributed optimization problems, respectively, by fully exploiting their structures and introducing consensus constraints. Inspired by primal-dual and consensus methods, we propose two distributed algorithms for the two cases with the help of projection and derivative feedback techniques. Furthermore, we prove that the algorithms converge to their optimal solutions, and moreover, their convergences rates are evaluated by the duality gap. (C) 2021 Elsevier Ltd. All rights reserved.
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收藏
页数：7
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