This book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike.

Introduces readers to state-of-the-art and advanced distributed optimization algorithms in networked systems Proposes effective strategies for rapid convergence and efficient execution of distributed algorithms Presents efficient, practical algorithms validated by benchmark smart grid systems and online learning systems

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