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Practical Optimization by Philip E. Gill, Walter Murray and Margaret H. Wright (1982, Trade Paperback)

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This book is designed to help problem solvers make the best use of optimization software--i.e., to use existing methods most effectively whenever possible, and to adapt and modify techniques for particular problems if necessary. The contents of this book therefore include some topics that are essential for all those who wish to solve optimization problems. In addition, certain topics are treated that should be of special interest in most practical optimization problems. For example, advice is given to users who wish to take an active role in formulating their problems so as to enhance the chances of solving them successfully, and to users who need to understand why a certain method fails to solve their problem.

About this product

Product Identifiers

PublisherEmerald Publishing The Limited

ISBN-100122839528

ISBN-139780122839528

eBay Product ID (ePID)44365

Product Key Features

Number of Pages402 Pages

LanguageEnglish

Publication NamePractical Optimization

SubjectAlgebra / Linear, Calculus, Optimization

Publication Year1982

TypeTextbook

AuthorPhilip E. Gill, Walter Murray, Margaret H. Wright

Subject AreaMathematics

FormatTrade Paperback

Dimensions

Item Weight25 Oz

Item Length9.6 in

Item Width7.2 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN81-066366

Dewey Edition19

IllustratedYes

Dewey Decimal515

Table Of ContentIntroduction: Definition of Optimization Problems. Classification of Optimization Problems. Overview of Topics. Fundamentals: Introduction to Errors in Numerical Computation. Introduction to Numerical Linear Algebra. Linear Equations. Matrix Factorizations. Elements of Multivariate Analysis. Optimality Conditions: Characterization of a Minimum. Unconstrained Optimization. Linearly Constrained Optimization. Nonlinearly Constrained Optimization. Unconstrained Methods: Methods for Univariate Functions. Methods for Multivariate Non-Smooth Functions. Methods for Multivariate Smooth Functions. Second Derivative Methods. First Derivative Methods. Non-Derivative Methods for Smooth Functions. Methods for Sums of Squares. Methods for Large-Scale Problems. Linear Constraints: Methods for Linear Equality Constraints. Active Set Methods for Linear Inequality Constraints. Special Problem Categories. Problems with Few General Linear Constraints. Special Forms of the Constraints. Large-Scale Linearly Constrained Optimization. Finding an Initial Feasible Point. Implementation of Active Set Methods. Nonlinear Constraints: The Formulation of Algorithms. Penalty and Barrier Function Methods. Reduced-Gradient and Gradient-Projection Methods. Augmented Lagrangian Methods. Projected Lagrangian Methods. Lagrange Multiplier Estimates. Large-Scale Nonlinearly Constrained Optimization. Special Problem Categories. Modelling: Introduction. Classification of Optimization Problems. Avoiding Unnecessary Discontinuities. Problem Transformations. Scaling. Formulation of Constraints. Problems with Discrete or Integer Variables. Practicalities: Use of Software. Properties of the Computed Solution. Assessment of Results. What Can Go Wrong (and what to do about it). Estimating the Accuracy of the Problem Functions. Computing Finite Differences. More About Scaling. Questions and Answers. Bibliography. Index.

SynopsisThis book is designed to help problem solvers make the best use of optimization software--i.e., to use existing methods most effectively whenever possible, and to adapt and modify techniques for particular problems if necessary. The contents of this book therefore include some topics that are essential for all those who wish to solve optimization problems. In addition, certain topics are treated that should be of special interest in most practical optimization problems. For example, advice is given to users who wish to take an active role in formulating their problems so as to enhance the chances of solving them successfully, and to users who need to understand why a certain method fails to solve their problem.

LC Classification NumberQA402.5.G54 1981