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Practical Mathematical Optimization

An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms

  • Jan A. Snyman

Part of the Applied Optimization book series (APOP, volume 97)

Table of contents

About this book

Introduction

This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form without neglecting rigour. The work should enable the professional to apply optimization theory and algorithms to his own particular practical field of interest, be it engineering, physics, chemistry, or business economics. Most importantly, for the first time in a relatively brief and introductory work, due attention is paid to the difficulties—such as noise, discontinuities, expense of function evaluations, and the existence of multiple minima—that often unnecessarily inhibit the use of gradient-based methods. In a separate chapter on new gradient-based methods developed by the author and his coworkers, it is shown how these difficulties may be overcome without losing the desirable features of classical gradient-based methods.

 Audience

It is intended that this book be used in senior- to graduate-level semester courses in optimization, as offered in mathematics, engineering, computer science, and operations research departments, and also to be useful to practising professionals in the workplace.

Keywords

Mathematica algorithms linear optimization optimization programming

Authors and affiliations

  • Jan A. Snyman
    • 1
  1. 1.University of PretoriaPretoriaSouth Africa

Bibliographic information

  • DOI https://doi.org/10.1007/b105200
  • Copyright Information Springer Science+Business Media, Inc. 2005
  • Publisher Name Springer, Boston, MA
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-24348-1
  • Online ISBN 978-0-387-24349-8
  • Series Print ISSN 1384-6485
  • Buy this book on publisher's site