Linear convergence of event‐triggered distributed optimization with metric subregularity condition

This paper designs a continuous‐time algorithm with event‐triggered communication (ETC) for solving a class of distributed convex optimization problems with a metric subregularity condition. First, we develop an event‐triggered continuous‐time optimization algorithm to overcome the bandwidth limitat...

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Veröffentlicht in:Asian journal of control Jg. 27; H. 2; S. 750 - 764
Hauptverfasser: Yu, Xin, Cheng, Songsong, Qiu, Jianbin, Fan, Yuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc 01.03.2025
Schlagworte:
Algorithms
Convergence
Convexity
distributed optimization
event‐triggered
linear convergence
metric subregularity
Optimization
primal‐dual
ISSN:1561-8625, 1934-6093
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Zusammenfassung:This paper designs a continuous‐time algorithm with event‐triggered communication (ETC) for solving a class of distributed convex optimization problems with a metric subregularity condition. First, we develop an event‐triggered continuous‐time optimization algorithm to overcome the bandwidth limitation of multi‐agent systems. Besides, with the aid of Lyapunov theory, we prove that the distributed event‐triggered algorithm converges to the optimum set with an exact linear convergence rate, without the strongly convex condition. Moreover, we provide the discrete version of the continuous‐time algorithm and show its exact linear convergence rate. Finally, we give a comparison example to validate the effectiveness of the designed algorithm in communication resource saving.
Bibliographie:ObjectType-Article-1
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ISSN:1561-8625
1934-6093
DOI:10.1002/asjc.3467