Numerical Optimization

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ISBN-13:
9780387303031
Erscheinungsdatum:
01.09.2006
Seiten:
664
Autor:
Jorge Nocedal
Gewicht:
1448 g
Format:
260x184x44 mm
Serie:
Springer Series in Operations Research and Financial Engineering
Sprache:
Englisch
Beschreibung:

Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.
The new edition of this book presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. Its publication responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. This new edition has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are widely used in practice and are the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience including graduate students, researchers and practitioners. The authors have produced a text that is pleasant to read, informative and rigorous. It reveals both the beautiful nature of the discipline and its practical side.
Preface.-Preface to the Second Edition.-Introduction.-Fundamentals of Unconstrained Optimization.-Line Search Methods.-Trust-Region Methods.-Conjugate Gradient Methods.-Quasi-Newton Methods.-Large-Scale Unconstrained Optimization.-Calculating Derivatives.-Derivative-Free Optimization.-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 and Augmented Lagrangian Methods.-Sequential Quadratic Programming.-Interior-Point Methods for Nonlinear Programming.-Background Material.- Regularization Procedure.

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