Introduction to Global Optimization / Edition 2

Introduction to Global Optimization / Edition 2

by R. Horst
Pub. Date:
Springer US
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Introduction to Global Optimization / Edition 2

In this edition, the scope and character of the monograph did not change with respect to the first edition. Taking into account the rapid development of the field, we have, however, considerably enlarged its contents. Chapter 4 includes two additional sections 4.4 and 4.6 on theory and algorithms of D.C. Programming. Chapter 7, on Decomposition Algorithms in Nonconvex Optimization, is completely new. Besides this, we added several exercises and corrected errors and misprints in the first edition. We are grateful for valuable suggestions and comments that we received from several colleagues. R. Horst, P.M. Pardalos and N.V. Thoai March 2000 Preface to the First Edition Many recent advances in science, economics and engineering rely on nu­ merical techniques for computing globally optimal solutions to corresponding optimization problems. Global optimization problems are extraordinarily di­ verse and they include economic modeling, fixed charges, finance, networks and transportation, databases and chip design, image processing, nuclear and mechanical design, chemical engineering design and control, molecular biology, and environment al engineering. Due to the existence of multiple local optima that differ from the global solution all these problems cannot be solved by classical nonlinear programming techniques. During the past three decades, however, many new theoretical, algorith­ mic, and computational contributions have helped to solve globally multi­ extreme problems arising from important practical applications.

Product Details

ISBN-13: 9780792365747
Publisher: Springer US
Publication date: 10/31/2000
Series: Nonconvex Optimization and Its Applications (closed) Series , #48
Edition description: 2nd ed. 2001
Pages: 372
Product dimensions: 6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

Preface. 1. Fundamental Results on Convexity and Optimization. 2. Quadratic Programming. 3. General Concave Minimization. 4. D.C. Programming. 5. Lipschitz Optimization. 6. Global Optimization on Networks. Solutions. Selected References. Index.

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