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008			221022s2022 si | s |||| 0|eng d
020			_a9789811698408
024	7		_a10.1007/978-981-16-9840-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
100	1		_aLin, Zhouchen _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685019
245	1	0	_aAlternating Direction Method of Multipliers for Machine Learning _cby Zhouchen Lin, Huan Li, Cong Fang
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (XXIII, 263 páginas) _b1 ilustraciones
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aChapter 1. Introduction -- Chapter 2. Derivations of ADMM -- Chapter 3. ADMM for Deterministic and Convex Optimization -- Chapter 4. ADMM for Nonconvex Optimization -- Chapter 5. ADMM for Stochastic Optimization -- Chapter 6. ADMM for Distributed Optimization -- Chapter 7. Practical Issues and Conclusions.
520			_aMachine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time.
988			_aSpringer_Computer_2022
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9151819 _aAlgoritmos computacionales
700	1		_aLi, Huan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685020
700	1		_aFang, Cong. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685021
776	0	8	_iPrinted edition: _z9789811698392
776	0	8	_iPrinted edition: _z9789811698415
776	0	8	_iPrinted edition: _z9789811698422
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-9840-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b10/2022 _dz _eIG _zSI