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Practical methods of optimization
5
3

Fully describes optimization methods that are currently most valuable in solving reallife problems. Since optimization has applications in almost every branch of science and technology, the text emphasizes their practical aspects in conjunction with the heuristics useful in making them perform more reliably and efficiently. To this end, it presents comparative numerical studies to give readers a feel for possibile applications and to illustrate the problems in assessing evidence. Also provides theoretical background which provides insights into how methods are derived. This edition offers revised coverage of basic theory and standard techniques, with updated discussions of line search methods, Newton and quasiNewton methods, and conjugate direction methods, as well as a comprehensive treatment of restricted step or trust region methods not commonly found in the literature. Also includes recent developments in hybrid methods for nonlinear least squares; an extended discussion of linear programming, with new methods for stable updating of LU factors; and a completely new section on network programming. Chapters include computer subroutines, worked examples, and study questions.
From the Back Cover
This established textbook is noted for its coverage of optimization methods that are of practical importance. It provides a thorough treatment of standard methods such as linear and quadratic programming, Newtonlike methods and the conjugate gradient method. The theoretical aspects of the subject include an extended treatment of optimality conditions and the significance of Lagrange multipliers. The relevance of convexity theory to optimization is also not neglected. A significant proportion of the book is devoted to the solution of nonlinear problems, with an authoritative treatment of current methodology. Thus state of the art techniques such as the BFGS method, trust region methods and the SQP method are described and analysed. Other features are an extensive treatment of nonsmooth optimization and the L1 penalty function. Contents Part 1 Unconstrained Optimization Part 2 Constrained OptimizationIntroductionStructure of MethodsNewtonlike MethodsConjugate Direction MethodsRestricted Step MethodsSums of Squares and Nonlinear EquationsIntroductionLinear ProgrammingThe Theory of Constrained OptimizationQuadratic ProgrammingGeneral Linearly Constrained OptimizationNonlinear ProgrammingOther Optimization Problems
A classic and musthave for researchers using optimization
oriented reader (USA) 
I recommend this book for anybody needing to learn the basics of optimization as well as a reference for people already in the field. Dr. Fletcher covers the basics, advantages, and disadvantages of optimization methods capitalizing on his long experience on the field. I find the book to be clearly written and easy to follow. Although notation is clearly explained when introduced, the author gives a notation summary at the begining of the book. This makes the book usefull as a learning as well as a reference. Although the book has not changed since the second edition, the optimization methods it describes are still the mostwidelyapplicable ones.
Paperback reprint of 2nd Ed. from 1987
This is a good book from an award winning author and researcher in this subject. However, the 2000 publication date shown refers to the release of the paperback version. There is no new content since 1987 when the 2nd ed. was originally published in hardcover.I prefer Dennis and Schnabel's _Numerical Methods for Unconstrained Optimization and Nonlinear Equations_ for Newtonderived methods in unconstrained optimization, but Fletcher's book has the added material on constrained optimization (Part 2) that makes it more complete if one wants a single reference that covers the gamut of optimization.
Help you know what's under the hood
This is a very wellwritten book. It achieved both mathematical clearness and simplicity. I especially like the first part, unconstrained optimization. On the one hand, unconstrained optimization is very widely used. On the other hand, there isn't much content in it. Yet, there aren't many books explaining this small area clearly. This book starts with describing the structures of all methods, which gives you a sort of bird's eye view. It then devotes one chapter to each of the 3 major families of optimization methods. Comparing with another popular book, Numerical Recipes, which is orgnized by individual algorithms, this book will cerntainly gives the reader better understanding of the essence of all the algorithms. And as the name says, it's about 'practial' methods. You'll have no problem to implement the algorithms in the book and there are also many working examples to help you see how an algorithm works and help verify the implementation.


