In this paper, a systematic distributed optimization approach is proposed based on a fictitious play concept. The convergence of the algorithm is proven under the game theory framework. The result is equivalent to a consensus problem. It introduces a novel perspective to study the consensus problem. Such an equivalence is illustrated by numerical cases.


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    Titel :

    Consensus based on learning game theory


    Beteiligte:
    Lin, Zhongjie (Autor:in) / Liu, Hugh H. T. (Autor:in)


    Erscheinungsdatum :

    2014-08-01


    Format / Umfang :

    164755 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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