In recent years, due to extensive applications of robotics, sensors, power and wireless communication networks in the field of machine learning, signal processing and control, decentralized optimization has been widely studied [1–5]. Quite a few problems occurring in such fields can be transformed into distributed convex optimization problems on multi-agent networks, in which each single agent works together to minimize a shared cost function in the circumstance of various constraints. A salient feature of solving distributed convex optimization problems is that an agent only communicates with its in-neighbors. The communication architecture can be formulated as a network, often directed and/or unbalanced. These problems typically demand the design of optimization algorithms being completely distributed, that is, algorithms are implemented by agents without a central coordinator [6]. Moreover, these observations naturally need algorithms to solve problems that can work well merely based on local information exchange and are robust against changes of network topologies and potential link failures.


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    Titel :

    Asynchronous Broadcast-Based Distributed Optimization over Unbalanced Directed Networks


    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 :

    28 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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