Distributed Event-Triggered Nonconvex Optimization under Polyak-Lojasiewicz Condition

被引:0
作者
Gao, Chao [1 ]
Xu, Lei [1 ]
Zhang, Kunpeng [1 ]
Li, Yuzhe [1 ]
Liu, Zhiwei [2 ]
Yang, Tao [1 ]
机构
[1] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China
来源
2024 18TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION, ICARCV | 2024年
基金
中国国家自然科学基金;
关键词
Distributed nonconvex optimization; Dynamic event-triggered mechanism; Linear convergence; Polyak-Lojasiewicz condition; SYSTEMS;
D O I
10.1109/ICARCV63323.2024.10821649
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper considers the distributed nonconvex optimization problem, where the goal is to minimize the average of local nonconvex cost functions through local information exchange. Firstly, we propose a distributed optimization algorithm that integrates the gradient tracking method with a dynamic event-triggered communication scheme, thereby reducing communication overhead. Secondly, we demonstrate that the algorithm linearly converges to the global optimum under the Polyak-Lojasiewicz condition, which indicates that every stationary point is a global minimizer. The numerical experiment is presented to validate the theoretical results and confirm the algorithm's effectiveness.
引用
收藏
页码:930 / 935
页数:6
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