Distributed Proximal Algorithms for Multiagent Optimization With Coupled Inequality Constraints

• 成果类型: Article
• 署名作者: Li, Xiuxian; Feng, Gang; Xie, Lihua
• 署名单位: City University of Hong Kong; Nanyang Technological University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.2989282
• 发表日期: 2021
• 页码: 1223-1230
• 关键词: Coupled inequality constraints; distributed optimization; multiagent networks; proximal point algorithm (PPA)

摘要

This article aims to address distributed optimization problems over directed and time-varying networks, where the global objective function consists of a sum of locally accessible convex objective functions subject to a feasible set constraint and coupled inequality constraints whose information is only partially accessible to each agent. For this problem, a distributed proximal-based algorithm, called distributed proximal primal-dual algorithm, is proposed based on the celebrated centralized proximal point algorithm. It is shown that the proposed algorithm can lead to the global optimal solution with a general step size, which is diminishing and nonsummable, but not necessarily square summable, and the saddle-point running evaluation error vanishes proportionally to O(1/root k), where k > 0 is the iteration number. Finally, a simulation example is presented to corroborate the effectiveness of the proposed algorithm.