标题：Distributed Convex Optimization Consensus in Multi-Agent Network Subject to Equality Constraints
作者：Zhao, Daduan; Dong, Tao; Li, XiaoLi; Li, Yan
作者机构：[Zhao, Daduan; Dong, Tao; Li, XiaoLi] Southwest Univ, Coll Elect & Informat Engn, Chongqing 400715, Peoples R China.; [Li, Yan] Shandong Univ, Sch C 更多
会议名称：IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)
会议日期：MAY 25-27, 2018
来源：PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS)
关键词：Distributed Cooperative; Convex Optimization; Multi-Agent Network;; Consensus
摘要：By employing the Lyapunov direct method, this article investigated the distributed convex optimization consensus problem for multi-agent network subject to equality constraints, where each agent is comprised with an individual objective function which is coercive and convex. More specifically, a novel optimization consensus algorithm based on the gradient projection operator and the method of exploiting penalty is proposed. Furthermore, it is proved that for any initial state, the algorithm can achieve the consensus, and in the meanwhile reach the minimum of the aggregate objective functions within the constraint set. At last, a numerical example is provided to validate the effectiveness of the proposed optimization consensus algorithm.