文献类型：期刊文献

英文题名：Distributed Nonconvex Event-Triggered Optimization Over Time-Varying Directed Networks

作者：Mao, Shuai[1];Dong, Ziwei[1];Du, Wei[1];Tian, Yu-Chu[2];Liang, Chen[1];Tang, Yang[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld 4001, Australia

年份：2022

卷号：18

期号：7

起止页码：4737

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20221812063128);WOS:【SCI-EXPANDED(收录号:WOS:000784218500044)】；

基金：This work was supported in part by the National Natural Science Foundation of China (Basic Science Center Program) under Grant 61988101, in part by International (Regional) Cooperation and Exchange Project under Grant 61720106008, in part by the National Natural Science Foundation of China under Grant 61725301, Grant 61925305 and Grant 62173144, in part by the Program of Shanghai Academic Research Leader under Grant 20XD1401300, in part by the Natural Science Foundation of Shanghai under Grant 21ZR1416100, and by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Distributed optimization; event-triggered scheme; nonconvex optimization; time-varying directed networks

摘要：Many problems in industrial smart manufacturing, such as process operational optimization and decision-making, can be regarded as distributed nonconvex optimization problems, whose goal is to utilize distributed nodes to cooperatively search for the minimal value of the global objective function. With the consideration of data transmission mode, transmission condition, and communication waste in industrial applications, it is meaningful to study the distributed nonconvex optimization problem with an event-triggered strategy over time-varying directed networks. To solve such a problem, a distributed nonconvex event-triggered algorithm is proposed in this article. Under some assumptions on local objective functions, gradients, and step sizes, the convergence of the proposed event-triggered algorithm to the local minimum is established theoretically. Moreover, it is obtained that the proposed distributed event-triggered algorithm has a convergence rate of O(1/ ln(t)). Finally, two examples of industrial systems are provided to validate the effectiveness of the proposed algorithm