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

Jan Snyman

ISBN 10: 038729824X / ISBN 13: 9780387298245

Published by Springer US, 2005

About this Item

Practical Mathematical Optimization | An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms | Jan Snyman | Taschenbuch | xx | Englisch | 2005 | Springer US | EAN 9780387298245 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Seller Inventory # 102236486

Bibliographic Details

• Title: Practical Mathematical Optimization | An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms
• Publisher: Springer US
• Publication Date: 2005
• Language: English
• ISBN 10: 038729824X
• ISBN 13: 9780387298245
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 16D millimeters 235 millimeters width by 155 millimeters height by 16 millimeters depth
• Weight: 429 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

It is intended that this book be used in senior- to graduate-level semester courses in optimization, as offered in mathematics, engineering, com­ puter science and operations research departments. Hopefully this book will also be useful to practising professionals in the workplace. The contents of the book represent the fundamental optimization mate­ rial collected and used by the author, over a period of more than twenty years, in teaching Practical Mathematical Optimization to undergradu­ ate as well as graduate engineering and science students at the University of Pretoria. The principal motivation for writing this work has not been the teaching of mathematics per se, but to equip students with the nec­ essary fundamental optimization theory and algorithms, so as to enable them to solve practical problems in their own particular principal fields of interest, be it physics, chemistry, engineering design or business eco­ nomics. The particular approach adopted here follows from the author's own personal experiences in doing research in solid-state physics and in mechanical engineering design, where he was constantly confronted by problems that can most easily and directly be solved via the judicious use of mathematical optimization techniques. This book is, however, not a collection of case studies restricted to the above-mentioned specialized research areas, but is intended to convey the basic optimization princi­ ples and algorithms to a general audience in such a way that, hopefully, the application to their own practical areas of interest will be relatively simple and straightforward.

From the Back Cover

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.