Distributed nonlinear consensus in the space of probability measures

Adrian N. Bishop, Arnaud Doucet

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    10 Citations (Scopus)

    Abstract

    Distributed consensus in the Wasserstein metric space of probability measures is introduced for the first time in this work. It is shown that convergence of the individual agents' measures to a common measure value is guaranteed so long as a weak network connectivity condition is satisfied asymptotically. The common measure achieved asymptotically at each agent is the one closest simultaneously to all initial agent measures in the sense that it minimises a weighted sum of Wasserstein distances between it and all the initial measures. This algorithm has applicability in the field of distributed estimation.

    Original languageEnglish
    Title of host publication19th IFAC World Congress IFAC 2014, Proceedings
    EditorsEdward Boje, Xiaohua Xia
    PublisherIFAC Secretariat
    Pages8662-8668
    Number of pages7
    ISBN (Electronic)9783902823625
    DOIs
    Publication statusPublished - 2014
    Event19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014 - Cape Town, South Africa
    Duration: 24 Aug 201429 Aug 2014

    Publication series

    NameIFAC Proceedings Volumes (IFAC-PapersOnline)
    Volume19
    ISSN (Print)1474-6670

    Conference

    Conference19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014
    Country/TerritorySouth Africa
    CityCape Town
    Period24/08/1429/08/14

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