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008			230208s2023 si | o |||| 0|eng d
020			_a9789811985591
024	7		_a10.1007/978-981-19-8559-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5105.5 _b2023 EB
100	1		_aLü, Qingguo _eautor _0(orcid)0000-0003-3602-0946 _1https://orcid.org/0000-0003-3602-0946 _4http://id.loc.gov/vocabulary/relators/aut _9689449
245	1	0	_aDistributed Optimization in Networked Systems : _bAlgorithms and Applications _cby Qingguo Lü, Xiaofeng Liao, Huaqing Li, Shaojiang Deng, Shanfu Gao
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aWireless Networks _x2366-1445
505	0		_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.
520			_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.
988			_aSpringer_Computer_2023
650		7	_2embne _9141354 _aRedes informáticas
700	1		_9689450 _aLiao, Xiaofeng _eautor
700	1		_9689451 _aLi, Huaqing _c(Professor of computer science) _eautor
700	1		_9689452 _aDeng, Shaojiang _eautor
700	1		_9689453 _aGao, Shanfu _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-8559-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _eb _zSI