Springer Distributed Optimization: Advances in Theories, Methods, and Applications

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. From the Back Cover 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. About the Author Huaqing Li, Professor, College of Electronic and Information Engineering, Southwest University, Chongqing, China. He received the B.S. degree in Information and Computing Science from Chongqing University of Posts and Telecommunications, in 2009, and the Ph.D. degree in Computer Science and Technology from Chongqing University in 2013. He was a Postdoctoral Researcher at the School of Electrical and Information Engineering, The University of Sydney from September 2014 to September 2015, and at the School of Electrical and Electronic Engineering, Nanyang Technological University from November 2015 to November 2016. From July 2018, he was a Professor at the College of Electronic and Information Engineering, Southwest University, Chongqing, China. His main research interests include nonlinear dynamics and control, multi-agent systems, and distributed optimization. Prof. Li currently serves as a Regional Editor for Neural Computing & Applications and an Editorial Board Member for IEEE Access. Qingguo Lü, College of Electronic and Information Engineering, Southwest University, Chongqing, China. He received the B.S. degree in Measurement and Control Technology and Equipment from the Anhui University of Technology, Anhui, China, in 2014, and the M.S. degree in Signal and Information Pr