Distributed strategies for average consensus in directed graphs

We address the average consensus problem for a distributed system whose components (nodes) can exchange information via interconnections (links) that form an arbitrary, strongly connected but possibly directed, topology (graph). Specifically, we discuss how the nodes can asymptotically reach average...

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Vydáno v:2011 50th IEEE Conference on Decision and Control and European Control Conference s. 2124 - 2129
Hlavní autoři: Dominguez-Garcia, Alejandro D., Hadjicostis, Christoforos N.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2011
Témata:
Convergence
Directed graphs
Distributed algorithms
Eigenvalues and eigenfunctions
Europe
Iterative algorithms
Topology
US Government
Weighted sum model
Wireless networks
ISBN:9781612848006, 1612848001
ISSN:0191-2216
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Abstract We address the average consensus problem for a distributed system whose components (nodes) can exchange information via interconnections (links) that form an arbitrary, strongly connected but possibly directed, topology (graph). Specifically, we discuss how the nodes can asymptotically reach average consensus (i.e., obtain the average of their initial values) with linear-iterative algorithms in which each node updates its value using a weighted linear combination of its own value and the values of neighboring nodes. In the process, the strategies we develop allow the nodes to adapt their weights in a distributed fashion, so that asymptotically they obtain a doubly stochastic weight matrix, which is useful for many algorithms that utilize linear- or nonlinear-iterative schemes to perform various estimation and optimization tasks.
AbstractList We address the average consensus problem for a distributed system whose components (nodes) can exchange information via interconnections (links) that form an arbitrary, strongly connected but possibly directed, topology (graph). Specifically, we discuss how the nodes can asymptotically reach average consensus (i.e., obtain the average of their initial values) with linear-iterative algorithms in which each node updates its value using a weighted linear combination of its own value and the values of neighboring nodes. In the process, the strategies we develop allow the nodes to adapt their weights in a distributed fashion, so that asymptotically they obtain a doubly stochastic weight matrix, which is useful for many algorithms that utilize linear- or nonlinear-iterative schemes to perform various estimation and optimization tasks.
Author Hadjicostis, Christoforos N.
Dominguez-Garcia, Alejandro D.
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  fullname: Dominguez-Garcia, Alejandro D.
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  givenname: Christoforos N.
  surname: Hadjicostis
  fullname: Hadjicostis, Christoforos N.
  email: chadjic@UCY.AC.CY
  organization: Department of Electrical and Computer Engineering at the University of Cyprus, Nicosia, Cyprus
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StartPage 2124
SubjectTerms Convergence
Directed graphs
Distributed algorithms
Eigenvalues and eigenfunctions
Europe
Iterative algorithms
Topology
US Government
Weighted sum model
Wireless networks
Title Distributed strategies for average consensus in directed graphs
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