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Autor(en): 
  • Zheng Wang
  • Xiaofeng Liao
  • Tingwen Huang
  • Huaqing Li
  • Qingguo Lü
  • Distributed Optimization: Advances in Theories, Methods, and Applications 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  August 2020  
    Genre:  Naturwissensch., Medizin, Technik 
     
    asynchronouscommunication / Distributedalgorithms / DistributedOptimization / dynamicnetworks / EconomicDispatch / Event-triggeredscheme / machinelearning / networkedcontrolsystem
    ISBN:  9789811561085 
    EAN-Code: 
    9789811561085 
    Verlag:  Springer 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 241 mm / B 160 mm / D 20 mm 
    Gewicht:  565 gr 
    Seiten:  264 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike. Focusing on the natures and functions of agents, communication networks and algorithms in the context of distributed optimization for networked control systems, this book introduces readers to the background of distributed optimization; recent developments in distributed algorithms for various types of underlying communication networks; the implementation of computation-efficient and communication-efficient strategies in the execution of distributed algorithms; and the frameworks of convergence analysis and performance evaluation. On this basis, the book then thoroughly studies 1) distributed constrained optimization and the random sleep scheme, from an agent perspective; 2) asynchronous broadcast-based algorithms, event-triggered communication, quantized communication, unbalanced directed networks, and time-varying networks, froma communication network perspective; and 3) accelerated algorithms and stochastic gradient algorithms, from an algorithm perspective. Finally, the applications of distributed optimization in large-scale statistical learning, wireless sensor networks, and for optimal energy management in smart grids are discussed.

      



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