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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Asian journal of control Ročník 27; číslo 2; s. 750 - 764
Hlavní autoři: Yu, Xin, Cheng, Songsong, Qiu, Jianbin, Fan, Yuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Wiley Subscription Services, Inc 01.03.2025
Témata:
Algorithms
Convergence
Convexity
distributed optimization
event‐triggered
linear convergence
metric subregularity
Optimization
primal‐dual
ISSN:1561-8625, 1934-6093
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1561-8625
1934-6093
DOI:10.1002/asjc.3467