Fast Linear Iterations for Distributed Averaging

Lin Xiao, Stephen Boyd

Research output: Contribution to journalConference articlepeer-review

209 Scopus citations

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 simple heuristics that are based on the Laplacian matrix of the associated graph.

Original languageEnglish
Pages (from-to)4997-5002
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume5
StatePublished - 2003
Externally publishedYes
Event42nd IEEE Conference on Decision and Control - Maui, HI, United States
Duration: Dec 9 2003Dec 12 2003

Keywords

  • Distributed consensus
  • Linear system
  • Semidefinite program
  • Spectral radius

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