Distributed convex optimization based on zero‐gradient‐sum algorithm under switching topology

This paper designs a finite‐time convergence protocol and an event‐triggered control protocol based on Zero‐Gradient‐Sum (ZGS) algorithm under stochastic switching undirected topology, respectively, which greatly expands the theory of continuous‐time distributed optimization algorithms. With finite‐...

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Published inIET control theory & applications Vol. 17; no. 12; pp. 1611 - 1624
Main Authors Tan, Manchun, Ren, Junwu, Ye, Lei, Xiang, Jianglian
Format Journal Article
LanguageEnglish
Published Stevenage John Wiley & Sons, Inc 01.08.2023
Wiley
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ISSN1751-8644
1751-8652
1751-8652
DOI10.1049/cth2.12497

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Summary:This paper designs a finite‐time convergence protocol and an event‐triggered control protocol based on Zero‐Gradient‐Sum (ZGS) algorithm under stochastic switching undirected topology, respectively, which greatly expands the theory of continuous‐time distributed optimization algorithms. With finite‐time stability and Lyapunov stability analysis, it is illustrated that the proposed method can finite‐time converge to the optimal solution of distributed unconstrained convex optimization problem and overcome the disturbances of the switching communication networks. In addition, the event‐triggered mechanism can effectively reduce the network burden and communication cost as well as avoid Zeno behaviour. Finally, two numerical simulations verify the advantages and effectiveness of these methods. This paper designs a finite‐time convergence protocol and an event‐triggered control protocol based on Zero‐Gradient‐Sum (ZGS) algorithm under stochastic switching undirected topology, respectively, which greatly expands the theory of continuous‐time distributed optimization algorithms. With finite‐time stability and Lyapunov stability analysis, it is illustrated that the proposed method can converge to the optimal solution of distributed unconstrained convex optimization problem and overcome the disturbances of the switching communication networks.
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ISSN:1751-8644
1751-8652
1751-8652
DOI:10.1049/cth2.12497