文献类型：期刊文献

中文题名：A Linear Algorithm for Quantized Event-Triggered Optimization Over Directed Networks

作者：Yang Yuan[1];Liyu Shi[1];Wangli He[1,2]

机构：[1]Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China;[2]IEEE

年份：2022

卷号：9

期号：6

起止页码：1095

中文期刊名：IEEE/CAA Journal of Automatica Sinica

外文期刊名：自动化学报（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2021_2022】；

基金：supported by National Natural Science Foundation of China(61988101,61922030,61890930-3);Shanghai International Science Technology Cooperation Program(21550712400)。

语种：英文

中文关键词：solution;optimal;letter;

摘要：Dear Editor,This letter investigates a class of distributed optimization problems with constrained communication.A quantized discrete-time eventtriggered zero-gradient-sum algorithm(QDE-ZGS)is developed to optimize the sum of local functions over weight-balanced directed networks.Based on an encoder-decoder scheme and a zooming-in technique,an event-triggered quantization communication is designed.Theoretical analysis shows that the exact convergence to the global optimal solution is guaranteed when the triggering threshold is bounded and the scaled sequence introduced by the zooming-in technique is quadratic summable.When the scaled sequence is bounded by an exponential decay function,QDE-ZGS converges linearly to the unique global optimal solution.Numerical simulations are conducted to demonstrate the theoretical results.