title: Practical Methods of Optimization
author:  R. Fletcher 
description: This established textbook is noted for its coverage of optimizationmethods that are of practical importance. It provides a thoroughtreatment of standard methods such as linear and quadraticprogramming, Newton-like methods and the conjugate gradient method.The theoretical aspects of the subject include an extendedtreatment of optimality conditions and the significance of Lagrangemultipliers. The relevance of convexity theory to optimization isalso not neglected. A significant proportion of the book is devotedto the solution of nonlinear problems, with an authoritativetreatment of current methodology. Thus state of the art techniquessuch as the BFGS method, trust region methods and the SQP methodare described and analysed. Other features are an extensivetreatment of nonsmooth optimization and the L_1 penalty function.Contents Part 1 Unconstrained Optimization Part 2 ConstrainedOptimization<br />* Introduction<br />* Structure of Methods<br />* Newton-like Methods<br />* Conjugate Direction Methods<br />* Restricted Step Methods<br />* Sums of Squares and Nonlinear Equations<br />* Introduction<br />* Linear Programming<br />* The Theory of Constrained Optimization<br />* Quadratic Programming<br />* General Linearly Constrained Optimization<br />* Nonlinear Programming<br />* Other Optimization Problems
zlib_text: 2 
   
  Kamran Iqbal
  Fundamental Engineering Optimization 
  Methods
  Download free eBooks at bookboon.com
  
  3 
   
  Fundamental Engineering Optimization Methods
  1st edition
  © 2013 Kamran Iqbal & bookboon.com
  ISBN 978-87-403-0489-3
  Download free eBooks at bookboon.com
  
  Fundamental Engineering Optimization 
  Methods
  4 
  Contents
  Contents
   
  Preface 
  8
  1 
   Engineering Design Optimization 
  10
  1.1 
  Introduction  
  10
  1.2 
  Optimization Examples in Science and Engineering 
  11
  1.3 
  Notation 
  18
  2 
  Mathematical Preliminaries 
  19
  2.1 
  Set Definitions  
  19
  2.2 
  Function Definitions 
  20
  2.3 
  Taylor Series Approximation 
  21
  2.4 
  Gradient Vector and Hessian Matrix 
  23
  2.5 
  Convex Optimization Problems 
  24
  2.6 
  Vector and Matrix Norms 
  26
  2.7 
  Matrix Eigenvalues and Singular Values 
  26
  2.8 
  Quadratic Function Forms 
  27
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