Event-triggered asynchronous distributed optimization algorithm with heterogeneous time-varying step-sizes

被引：0
作者：
Tangtang Xie
Guo Chen
Xiaofeng Liao
机构：
[1] Southwest University,Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering
[2] The University of New South Wales,School of Electrical Engineering and Telecommunications
来源：
Neural Computing and Applications | 2020年 / 32卷
关键词：
Distributed optimization; Asynchronous algorithm; Event-triggered scheme; Heterogeneous time-varying step-sizes; Linear convergence;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper concerns distributed convex optimization problems over time-varying undirected graphs, in which the global objective function is expressed as the sum of individual objective functions of the agents. Each agent only knows its local objective functions. To figure out such problems, an event-triggered asynchronous distributed optimization algorithm (termed as EV-ADOA) with time-varying heterogeneous step-sizes is proposed, which is suitable for undirected graphs changing over time. Under two standard assumptions on strongly convex and smoothness of local objective functions, the EV-ADOA can achieve linear convergence with a proper upper bound of the heterogeneous time-varying step-sizes. EV-ADOA with event-triggered scheme can decrease network communication, and the Zeno-like behavior strictly is excluded. The efficiency of EV-ADOA is demonstrated by experiments.
引用
收藏
页码：6175 / 6184
页数：9
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