A Class of Distributed Online Aggregative Optimization in Unknown Dynamic Environment

被引：0
|
作者：
Yang, Chengqian [1 ]
Wang, Shuang [1 ]
Zhang, Shuang [2 ]
Lin, Shiwei [2 ]
Huang, Bomin [2 ]
机构：
[1] Northeastern Univ Qinhuangdao, Sch Control Engn, Qinhuangdao 066004, Peoples R China
[2] Jimei Univ, Coll Comp Engn, Xiamen 361021, Peoples R China
来源：
MATHEMATICS | 2024年 / 12卷 / 16期
基金：
国家重点研发计划;
关键词：
online optimization; aggregated terms; distributed algorithm; dynamic environment;
D O I：
10.3390/math12162460
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
This paper considers a class of distributed online aggregative optimization problems over an undirected and connected network. It takes into account an unknown dynamic environment and some aggregation functions, which is different from the problem formulation of the existing approach, making the aggregative optimization problem more challenging. A distributed online optimization algorithm is designed for the considered problem via the mirror descent algorithm and the distributed average tracking method. In particular, the dynamic environment and the gradient are estimated by the averaged tracking methods, and then an online optimization algorithm is designed via a dynamic mirror descent method. It is shown that the dynamic regret is bounded in the order of O(T). Finally, the effectiveness of the designed algorithm is verified by some simulations of cooperative control of a multi-robot system.
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页数：15
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