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Distributed Optimization: Advances in Theories, Methods, and Applications (Hardcover, 1st ed. 2020)
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Distributed Optimization: Advances in Theories, Methods, and Applications (Hardcover, 1st ed. 2020)
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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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