Distributed Optimization: Advances in Theories, Methods, and Applications

This book provides a comprehensive reference guide for distributed optimization in networked control systems, covering agents, communication networks, and algorithms, with applications in large-scale statistical learning, wireless sensor networks, and smart grids.

Format: Paperback / softback
Length: 243 pages
Publication date: 06 August 2021
Publisher: Springer Verlag, Singapore

This comprehensive book provides a valuable reference guide for researchers and senior undergraduate and graduate students in the field of distributed optimization. It delves into the natures and functions of agents, communication networks, and algorithms in the context of distributed optimization for networked control systems. The book introduces readers to the background of distributed optimization, recent developments in distributed algorithms, implementation strategies, convergence analysis, and performance evaluation. It then thoroughly studies various aspects of distributed optimization, including constrained optimization, random sleep scheme, asynchronous broadcast-based algorithms, event-triggered communication, quantized communication, unbalanced directed networks, and time-varying networks. Additionally, it explores accelerated algorithms and stochastic gradient algorithms. The book also discusses the applications of distributed optimization in large-scale statistical learning, wireless sensor networks, and optimal energy management in smart grids. With its comprehensive coverage and practical insights, this book is an essential resource for anyone interested in distributed optimization.

Weight: 409g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811561115
Edition number: 1st ed. 2020