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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    Title :

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


    Contributors:
    Li, Huaqing (author) / Lü, Qingguo (author) / Wang, Zheng (author) / Liao, Xiaofeng (author) / Huang, Tingwen (author)


    Publication date :

    2020-08-05


    Size :

    25 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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