Distributed Primal-Dual Subgradient Method for Multiagent Optimization via Consensus Algorithms

被引:197
作者
Yuan, Deming [1 ]
Xu, Shengyuan [1 ]
Zhao, Huanyu [1 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Jiangsu, Peoples R China
来源
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2011年 / 41卷 / 06期
基金
中国国家自然科学基金;
关键词
Average consensus; convex optimization; distributed optimization; subgradient method; NETWORKS; SYSTEMS; AGENTS;
D O I
10.1109/TSMCB.2011.2160394
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 studies the problem of optimizing the sum of multiple agents' local convex objective functions, subject to global convex inequality constraints and a convex state constraint set over a network. Through characterizing the primal and dual optimal solutions as the saddle points of the Lagrangian function associated with the problem, we propose a distributed algorithm, named the distributed primal-dual subgradient method, to provide approximate saddle points of the Lagrangian function, based on the distributed average consensus algorithms. Under Slater's condition, we obtain bounds on the convergence properties of the proposed method for a constant step size. Simulation examples are provided to demonstrate the effectiveness of the proposed method.
引用
收藏
页码:1715 / 1724
页数:10
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