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008			211031s2021 sz | o |||| 0|eng d
020			_a9783030854508
024	7		_a10.1007/978-3-030-85450-8 _2doi
040			_aES-VaU _bspa _cES-VaU _dES-VaU
050		4	_aT57.6-.97 _b2021 EB
100	1		_aLuenberger, David G. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	0	0	_aLinear and Nonlinear Programming _cby David G Luenberger, Yinyu Ye
250			_a5th ed. 2021.
264		1	_aCham _c2021 _bSpringer International Publishing
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aInternational Series in Operations Research & Management Science _x2214-7934 _v228
505	0		_a1. Introduction -- Part I: Linear Programming -- 2. Basic Properties of Linear Programs -- 3. Duality and Complementarity -- 4. The Simplex Method -- 5. Interior-Point Methods -- 6. Conic Linear Programming -- Part II: Unconstrained Problems -- 7. Basic Properties of Solutions and Algorithms -- 8. Basic Descent Methods -- 9. Conjugate Direction Methods -- 10. Quasi-Newton Methods -- Part III: Constrained Optimization -- 11. Constrained Optimization Conditions -- 12. Primal Methods -- 13. Penalty and Barrier Methods -- 14. Local Duality and Dual Methods -- 15. Primal-Dual Methods.
520			_aThe 5th edition of this classic textbook covers the central concepts of practical optimization techniques, with an emphasis on methods that are both state-of-the-art and popular. One major insight is the connection between the purely analytical character of an optimization problem and the behavior of algorithms used to solve that problem. End-of-chapter exercises are provided for all chapters. The material is organized into three separate parts. Part I offers a self-contained introduction to linear programming. The presentation in this part is fairly conventional, covering the main elements of the underlying theory of linear programming, many of the most effective numerical algorithms, and many of its important special applications. Part II, which is independent of Part I, covers the theory of unconstrained optimization, including both derivations of the appropriate optimality conditions and an introduction to basic algorithms. This part of the book explores the general properties of algorithms and defines various notions of convergence. In turn, Part III extends the concepts developed in the second part to constrained optimization problems. Except for a few isolated sections, this part is also independent of Part I. As such, Parts II and III can easily be used without reading Part I and, in fact, the book has been used in this way at many universities. New to this edition are popular topics in data science and machine learning, such as the Markov Decision Process, Farkas' lemma, convergence speed analysis, duality theories and applications, various first-order methods, stochastic gradient method, mirror-descent method, Frank-Wolf method, ALM/ADMM method, interior trust-region method for non-convex optimization, distributionally robust optimization, online linear programming, semidefinite programming for sensor-network localization, and infeasibility detection for nonlinear optimization.
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-85450-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Valencia)
942			_2lcc _cLE
988			_aSpringer_Business_2021
999			_c239169 _d239169