Numerical Optimization / Edition 2

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Overview

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.

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Editorial Reviews

From the Publisher
MMOR Mathematical Methods of Operations Research, 2001: "The books 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."
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Product Details

Table of Contents

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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Customer Reviews

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Sort by: Showing all of 2 Customer Reviews
  • Posted July 5, 2011

    One must-see book

    This book presents very clear pictures of fundamental theory and algorithms in numerical optimization. I especially like the way that it universally put Newton, Quazi-Newton, Conjugate Gradient methods into single framework and explains them in a very intuitive-friend way. To those who want to learn numerical optimization, I can only say: Read the Book.

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  • Anonymous

    Posted April 15, 2004

    This is a poorly organized book.

    I find this book to be nearly useless. The authors do not summarize algorithms nor do they present them clearly. One has to hunt through multiple chapters to understand a single algorithm. If one wants to understand an algorithm from chapter 10, one will have to go back and read several previous chapters such as chapters 3 & 4 due to the authors presentation style. An algorithm will be loosely introduced in an early chapter and then forward referenced to a much later chapter. The authors presentation style is to introduce all algorithms at a level too vague to be useful to any mathematical scientist or engineer in the first half of the book and then follow up with an incoherent presentation in the second half of the book. This book is a poor text book and a worse reference book. I do not recommend this book.

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