Convergence of Distributed Averaging and Maximizing Algorithms: Part II: State-dependent Graphs
2013 (English)In: 2013 American Control Conference, American Automatic Control Council , 2013, 6859-6864 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. In Part I of the paper, time-dependent graphs are studied. This part of the paper focuses on state-dependent graphs. We use a μ-nearest-neighbor rule, where each node interacts with its μ nearest smaller neighbors and the μ nearest larger neighbors. It is shown that μ+1 is a critical threshold on the total number of nodes for the transit from finite-time to asymptotic convergence for averaging, in the absence of node self-confidence. The threshold is 2μ if each node chooses to connect only to neighbors with unique values. The results characterize some similarities and differences between distributed averaging and maximizing algorithms.
Place, publisher, year, edition, pages
American Automatic Control Council , 2013. 6859-6864 p.
, Proceedings of the American Control Conference, ISSN 0743-1619
Averaging algorithms, Finite-time convergence, Max-consensus
IdentifiersURN: urn:nbn:se:kth:diva-133373ISI: 000327210207009ScopusID: 2-s2.0-84883517207ISBN: 978-147990177-7OAI: oai:DiVA.org:kth-133373DiVA: diva2:661959
2013 1st American Control Conference, ACC 2013; Washington, DC; United States; 17 June 2013 through 19 June 2013
QC 201311052013-11-052013-10-312014-01-20Bibliographically approved