Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms / Edition 1

by Jan Snyman

Overview

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. 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.

Product Details

ISBN-13:	9780387298245
Publisher:	Springer US
Publication date:	11/29/2005
Series:	Applied Optimization , #97
Edition description:	1st ed. 2005. Corr. 2nd printing 2005
Pages:	257
Product dimensions:	8.50(w) x 10.98(h) x (d)

Table of Contents

Line Search Descent Methods for Unconstrained Minimization.- Standard Methods for Constrained Optimization.- New Gradient-Based Trajectory and Approximation Methods.- Example Problems.- Some Theorems.