Over the past several years, under the great progress of multi-agent networks in emerging areas, increasing number of investigators have conducted in-depth research and achieved remarkable results. With the universalization of networked control systems, multi-agent networks not only introduce a theoretical analysis approach for modeling and analyzing dynamic systems, but also have a crucial role to play in studying distributed artificial intelligence [1–6]. Distributed coordination and optimization of networked control systems, as a significant topic in the study of multi-agent networks, have gained considerable interest and great attention. Specifically, this class of problem has found a number of engineering applications, e.g., distributed state estimation [7], resource allocation [8], regression [9, 10], as well as machine learning [11–13], among many others.


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    Titel :

    Achieving Linear Convergence of Distributed Optimization over Unbalanced Directed Networks with Row-Stochastic Weight Matrices


    Beteiligte:
    Li, Huaqing (Autor:in) / Lü, Qingguo (Autor:in) / Wang, Zheng (Autor:in) / Liao, Xiaofeng (Autor:in) / Huang, Tingwen (Autor:in)


    Erscheinungsdatum :

    05.08.2020


    Format / Umfang :

    25 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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    Distributed Economic Dispatch in Smart Grids: General Unbalanced Directed Network

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