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Distributed Optimization with Dynamic Event-Triggered Mechanisms
Univ North Texas, Dept Elect Engn, Denton, TX 76203 USA..
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
US Army Res Lab, Adelphi, MD 20783 USA..
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9940-5929
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2018 (English)In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2018, p. 969-974Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we consider the distributed optimization problem, whose objective is to minimize the global objective function, which is the sum of local convex objective functions, by using local information exchange. To avoid continuous communication among the agents, we propose a distributed algorithm with a dynamic event-triggered communication mechanism. We show that the distributed algorithm with the dynamic event-triggered communication scheme converges to the global minimizer exponentially, if the underlying communication graph is undirected and connected. Moreover, we show that the event-triggered algorithm is free of Zeno behavior. For a particular case, we also explicitly characterize the lower bound for inter-event times. The theoretical results are illustrated by numerical simulations.

Place, publisher, year, edition, pages
IEEE , 2018. p. 969-974
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-245014DOI: 10.1109/CDC.2018.8619311ISI: 000458114800137Scopus ID: 2-s2.0-85062191415ISBN: 978-1-5386-1395-5 (print)OAI: oai:DiVA.org:kth-245014DiVA, id: diva2:1293673
Conference
57th IEEE Conference on Decision and Control (CDC), DEC 17-19, 2018, Miami Beach, FL
Note

QC 20190305

Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-04-11Bibliographically approved

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Yi, XinleiJohansson, Karl H.

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CiteExportLink to record
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