Convergence of distributed averaging and maximizing algorithms: Part I: Time-dependent graphs
2013 (English)In: 2013 American Control Conference (ACC), American Automatic Control Council , 2013, 6096-6101 p.Conference paper (Refereed)
In this paper, we formulate and investigate a generalized consensus algorithm which makes an attempt to unify distributed averaging and maximizing algorithms considered in the literature. Each node iteratively updates its state as a time-varying weighted average of its own state, the minimal state, and the maximal state of its neighbors. This part of the paper focuses on time-dependent communication graphs. We prove that finite-time consensus is almost impossible for averaging under this uniform model. Then various necessary and/or sufficient conditions are presented on the consensus convergence. The results characterize some similarities and differences between distributed averaging and maximizing algorithms.
Place, publisher, year, edition, pages
American Automatic Control Council , 2013. 6096-6101 p.
, Proceedings of the American Control Conference, ISSN 0743-1619
Averaging algorithms, Finite-time convergence, Max-consensus
IdentifiersURN: urn:nbn:se:kth:diva-133384ISI: 000327210206048ScopusID: 2-s2.0-84883548047ISBN: 978-147990177-7OAI: oai:DiVA.org:kth-133384DiVA: diva2:661554
2013 1st American Control Conference, ACC 2013; Washington, DC; United States; 17 June 2013 through 19 June 2013
QC 201311042013-11-042013-10-312014-01-20Bibliographically approved