Integrated Methods for Optimization
The first edition of Integrated Methods for Optimization was published in January 2007. Because the book covers a rapidly developing field, the time is right for a second edition. The book provides a unified treatment of optimization methods. It brings id
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Volume 170
Series Editor: Frederick S. Hillier Stanford University, CA, USA Special Editorial Consultant: Camille C. Price Stephen F. Austin State University, TX, USA
For further volumes www.springer.com/series/6161
John N. Hooker
Integrated Methods for Optimization Second Edition
John N. Hooker Carnegie Mellon University Tepper School of Business Pittsburgh, Pennsylvania USA [email protected]
ISSN 0884-8289 ISBN 978-1-4614-1899-3 e-ISBN 978-1-4614-1900-6 DOI 10.1007/978-1-4614-1900-6 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011940223 © Springer Science+Business Media, LLC 2012 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Optimization has become a versatile tool in a wide array of application areas, ranging from manufacturing and information technology to the social sciences. Methods for solving optimization problems are equally numerous and provide a large reservoir of problem-solving technology. In fact, there is such a variety of methods that it is difficult to take full advantage of them. They are described in different technical languages and are implemented in different software packages. Many are not implemented at all. It is hard to tell which one is best for a given problem, and there is too seldom an opportunity to combine techniques that have complementary strengths. The ideal would be to bring these methods under one roof, so that they and their combinations are all available to solve a problem. As it turns out, many of them share, at some level, a common problemsolving strategy. This opens the door to integration—to the design of a modeling and algorithmic framework within which different techniques can work together in a principled way. This book undertakes such a project. It deals primarily with the unification of mathematical programming and constraint programming, since this has been the focus of most recent research on integrated methods. Mathematical programming brings to the table its sophisticated relaxation techniques and concepts of duality. Constraint programming contributes its inference and propagation methods, along with a powerful modeling approach. It is possible to have all of these advantages at once, rather than being forced to choose between them. Contin
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