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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    Title :

    Consensus based on learning game theory


    Contributors:


    Publication date :

    2014-08-01


    Size :

    164755 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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