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Distributed Optimization Advances In Theories Methods And Applications 1st Ed Huaqing Li

  • SKU: BELL-22476542
Distributed Optimization Advances In Theories Methods And Applications 1st Ed Huaqing Li
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Distributed Optimization Advances In Theories Methods And Applications 1st Ed Huaqing Li instant download after payment.

Publisher: Springer Singapore;Springer
File Extension: PDF
File size: 6.3 MB
Author: Huaqing Li, Qingguo Lü, Zheng Wang, Xiaofeng Liao, Tingwen Huang
ISBN: 9789811561085, 9789811561092, 9811561087, 9811561095
Language: English
Year: 2020
Edition: 1st ed.

Product desciption

Distributed Optimization Advances In Theories Methods And Applications 1st Ed Huaqing Li by Huaqing Li, Qingguo Lü, Zheng Wang, Xiaofeng Liao, Tingwen Huang 9789811561085, 9789811561092, 9811561087, 9811561095 instant download after payment.

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, from a 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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