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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.
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