03700nam 22006015 450 991065948440332120251009082059.09789811985591981198559610.1007/978-981-19-8559-1(MiAaPQ)EBC7194602(Au-PeEL)EBL7194602(CKB)26130493300041(DE-He213)978-981-19-8559-1(PPN)268206937(EXLCZ)992613049330004120230208d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDistributed Optimization in Networked Systems Algorithms and Applications /by Qingguo Lü, Xiaofeng Liao, Huaqing Li, Shaojiang Deng, Shanfu Gao1st ed. 2023.Singapore :Springer Nature Singapore :Imprint: Springer,2023.1 online resource (282 pages)Wireless Networks,2366-1445Print version: Lü, Qingguo Distributed Optimization in Networked Systems Singapore : Springer,c2023 9789811985584 Includes bibliographical references.Chapter 1. Distributed Nesterov-Like Accelerated Algorithms in Networked Systems with Directed Communications -- Chapter 2. Distributed Stochastic Projected Gradient Algorithms for Composite Constrained Optimization in Networked Systems -- Chapter 3. Distributed Proximal Stochastic Gradient Algorithms for Coupled Composite Optimization in Networked Systems -- Chapter 4. Distributed Subgradient Algorithms Based on Event-Triggered Strategy in Networked Systems -- Chapter 5. Distributed Accelerated Stochastic Algorithms Based on Event-Triggered Strategy in Networked Systems -- Chapter 6. Event-Triggered Based Distributed Optimal Economic Dispatch in Smart Grids -- Chapter 7. Fast Distributed Optimal Economic Dispatch in Dynamic Smart Grids with Directed Communications -- Chapter 8. Accelerated Distributed Optimal Economic Dispatch in Smart Grids with Directed Communications -- Chapter 9. Privacy Preserving Distributed Online Learning with Time-Varying and Directed Communications.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.Wireless Networks,2366-1445AlgorithmsMachine learningComputer scienceDesign and Analysis of AlgorithmsMachine LearningTheory and Algorithms for Application DomainsAlgorithms.Machine learning.Computer science.Design and Analysis of Algorithms.Machine Learning.Theory and Algorithms for Application Domains.518.1Lü Qingguo1315666MiAaPQMiAaPQMiAaPQBOOK9910659484403321Distributed optimization in networked systems3363612UNINA