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＜電子ブック＞
Numerical Optimization / by Jorge Nocedal, Stephen Wright
(Springer Series in Operations Research and Financial Engineering. ISSN:21971773)

版	1st ed. 1999.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	1999
本文言語	英語
大きさ	XXI, 636 p : online resource
著者標目	*Nocedal, Jorge author Wright, Stephen author SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Calculus of variations LCSH:System theory LCSH:Control theory LCSH:Operations research FREE:Calculus of Variations and Optimization FREE:Systems Theory, Control FREE:Operations Research and Decision Theory
一般注記	Fundamentals of Unconstrained Optimization -- Line Search Methods -- Trust-Region Methods -- Conjugate Gradient Methods -- Practical Newton Methods -- Calculating Derivatives -- Quasi-Newton Methods -- Large-Scale Quasi-Newton and Partially Separable Optimization -- Nonlinear Least-Squares Problems -- Nonlinear Equations -- Theory of Constrained Optimization -- Linear Programming: The Simplex Method -- Linear Programming: Interior-Point Methods -- Fundamentals of Algorithms for Nonlinear Constrained Optimization -- Quadratic Programming -- Penalty, Barrier, and Augmented Lagrangian Methods -- Sequential Quadratic Programming This is a book for people interested in solving optimization problems. Because of the wide (and growing) use of optimization in science, engineering, economics, and industry, it is essential for students and practitioners alike to develop an understanding of optimization algorithms. Knowledge of the capabilities and limitations of these algorithms leads to a better understanding of their impact on various applications, and points the way to future research on improving and extending optimization algorithms and software. Our goal in this book is to give a comprehensive description of the most powerful, state-of-the-art, techniques for solving continuous optimization problems. By presenting the motivating ideas for each algorithm, we try to stimulate the reader’s intuition and make the technical details easier to follow. Formal mathematical requirements are kept to a minimum. Because of our focus on continuous problems, we have omitted discussion of important optimization topics such as discrete and stochastic optimization HTTP:URL=https://doi.org/10.1007/b98874

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