An event-triggering algorithm for decentralized stochastic optimization over networks |
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| Institution: | 1. College of Computer Science, Chongqing University, Chongqing 400044, PR China;2. College of Electronic and Information Engineering, Southwest University, Chongqing 400715, PR China;1. Department of Electrical Engineering, Shiraz University of Technology, Shiraz 71557-13876, Iran;2. Department of Electrical Engineering, University of Zanjan, Zanjan 45371-38791, Iran;3. Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliou, Yunlin 640301, Taiwan;1. Digital Economy Research Institute of Hangzhou Dianzi University-Yongjia, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;2. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China;3. Department of Electrical and Computer Engineering, COMSATS University Islamabad (Lahore Campus), Lahore 54000, Pakistan;1. Federal University of Minas Gerais, Graduate Program in Electrical Engineering, Av. Antonio Carlos, 6627, Belo Horizonte, MG 31270-901, Brazil;2. Federal University of Minas Gerais, Department of Electronics Engineering, Av. Antonio Carlos, 6627, Belo Horizonte, MG 31270-901, Brazil;3. Federal University of São João del-Rei, Department of Electrical Engineering, Praça Frei Orlando, 170, São João del-Rei, MG, Brazil;1. School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China;2. Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China;3. Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot, Nanjing, 210044, China;1. Department of Mathematics, Harbin Institute of Technology, Harbin 150001, China;2. School of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, China;1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Renmin Street No.5988, Changchun, China;2. Intelligent Connected Vehicle Development Institute, China Faw Group Co., Ltd, China |
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| Abstract: | In this paper, we study the problem of decentralized optimization to minimize a finite sum of local convex cost functions over an undirected network. Compared with the existing works, we focus on improving the communication efficiency of the stochastic gradient tracking method and propose an effective event-triggering decentralized stochastic gradient tracking algorithm, namely, ET-DSGT. ET-DSGT utilizes the event-triggering mechanism in which each agent only broadcasts its estimators at the event time to effectively avoid real-time communication, thus improving communication efficiency. In addition, we present a theoretical analysis to show that ET-DSGT with a decaying step-size can converge to the exact global minimum. Moreover, we show that for each agent, the time interval between two successive triggering times is greater than the iteration interval under certain conditions. Finally, we provide several simulations to demonstrate the effectiveness of ET-DSGT. |
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