LEADER 03700nam 22006015 450 001 9910659484403321 005 20251009082059.0 010 $a9789811985591 010 $a9811985596 024 7 $a10.1007/978-981-19-8559-1 035 $a(MiAaPQ)EBC7194602 035 $a(Au-PeEL)EBL7194602 035 $a(CKB)26130493300041 035 $a(DE-He213)978-981-19-8559-1 035 $a(PPN)268206937 035 $a(EXLCZ)9926130493300041 100 $a20230208d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDistributed Optimization in Networked Systems $eAlgorithms and Applications /$fby Qingguo Lü, Xiaofeng Liao, Huaqing Li, Shaojiang Deng, Shanfu Gao 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (282 pages) 225 1 $aWireless Networks,$x2366-1445 311 08$aPrint version: Lü, Qingguo Distributed Optimization in Networked Systems Singapore : Springer,c2023 9789811985584 320 $aIncludes bibliographical references. 327 $aChapter 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. 330 $aThis book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike. 410 0$aWireless Networks,$x2366-1445 606 $aAlgorithms 606 $aMachine learning 606 $aComputer science 606 $aDesign and Analysis of Algorithms 606 $aMachine Learning 606 $aTheory and Algorithms for Application Domains 615 0$aAlgorithms. 615 0$aMachine learning. 615 0$aComputer science. 615 14$aDesign and Analysis of Algorithms. 615 24$aMachine Learning. 615 24$aTheory and Algorithms for Application Domains. 676 $a518.1 700 $aLu?$b Qingguo$01315666 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910659484403321 996 $aDistributed optimization in networked systems$93363612 997 $aUNINA