Distributed Convex Optimization on State-Dependent Undirected Graphs: Homogeneity Technique

被引：0

作者：

Hong, Huifen ^{[1
]}

Yu, Xinghuo ^{[2
]}

Yu, Wenwu ^{[1
,3
]}

Zhang, Dong ^{[4
]}

Wen, Guanghui ^{[2
]}

机构：

[1] School of Mathematics, Southeast University, Nanjing,210096, China

[2] School of Engineering, RMIT University, Melbourne,VIC,3001, Australia

[3] Department of Electrical Engineering, Nantong University, Nantong,226002, China

[4] School of Astronautics, Northwestern Polytechnical University, Xi'an,710072, China

来源：

IEEE Transactions on Control of Network Systems | 2020年 / 7卷 / 01期

基金：

澳大利亚研究理事会; 中国国家自然科学基金;

关键词：

Convex optimization - Cost functions - Undirected graphs - Continuous time systems - Software agents;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This paper investigates the distributed convex optimization problem (DCOP) based on continuous-time multiagent systems under a state-dependent graph. The objective is to optimize the sum of local cost functions, each of which is only known by the corresponding agent. First, a piecewise continuous distributed optimization algorithm is proposed, such that all agents reach consensus in finite time and reach the optimal point of the total cost function asymptotically under a time-invariant graph. Then, another distributed optimization algorithm is presented to preserve the initial edges and make the agents solve DCOP on a state-dependent graph. In particular, any pair of agents can exchange information with each other when their geometry distance is less than a certain range. Finally, several simulations are given to verify the effectiveness of the proposed algorithms. © 2014 IEEE.

引用

页码：42 / 52

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