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Fast Linear Iterations for Distributed Averaging (2003)

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by Lin Xiao , Stephen Boyd
Venue:Systems and Control Letters
Citations:429 - 12 self
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BibTeX

@ARTICLE{Xiao03fastlinear,
    author = {Lin Xiao and Stephen Boyd},
    title = {Fast Linear Iterations for Distributed Averaging},
    journal = {Systems and Control Letters},
    year = {2003},
    volume = {53},
    pages = {65--78}
}

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Abstract

We consider the problem of finding a linear iteration that yields distributed averaging consensus over a network, i.e., that asymptotically computes the average of some initial values given at the nodes. When the iteration is assumed symmetric, the problem of finding the fastest converging linear iteration can be cast as a semidefinite program, and therefore efficiently and globally solved. These optimal linear iterations are often substantially faster than several common heuristics that are based on the Laplacian of the associated graph.

Keyphrases

linear iteration    distributed averaging    initial value    optimal linear iteration    associated graph    converging linear iteration    semidefinite program    several common heuristic   

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