Numerical optimization
著者
書誌事項
Numerical optimization
（Springer series in operations research）
Springer, c1999
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
"Numerical Optimization" presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. Drawing on their experiences in teaching, research, and consulting, the authors have produced a textbook that will be of interest to students and practitioners alike. Each chapter begins with the basic concepts and builds up gradually to the best techniques currently available. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. Above all, the authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side.
MMOR, Mathematical Methods of Operations Research, 2001 observes: "The book looks very suitable to be used in an graduate-level course in optimization for students in mathematics, operations research, engineering, and others. Moreover, it seems to be very helpful to do some self-studies in optimization, to complete own knowledge and can be a source of new ideas...I recommend this excellent book to everyone who is interested in optimization problems."
目次
Introduction * 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 * Background Material * References * Index.
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