Linear Programming and Generalizations A Problem-based Introduction
This book on constrained optimization is novel in that it fuses these themes: • use examples to introduce general ideas; • engage the student in spreadsheet computation; • survey the uses of constrained optimization;. • investigate game theory and nonline
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Volume 149
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: http://www.springer.com/series/6161
Eric V. Denardo
Linear Programming and Generalizations A Problem-based Introduction with Spreadsheets
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Eric V. Denardo Yale University P.O. Box 208267 New Haven CT 06520-8267 USA [email protected]
Additional material to this book can be downloaded from http://extra.springer.com. ISSN 0884-8289 ISBN 978-1-4419-6490-8 e-ISBN 978-1-4419-6491-5 DOI 10.1007/978-1-4419-6491-5 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011920997 © Springer Science+Business Media, LLC 2011 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
The title of this book adheres to a well-established tradition, but “linear programming and generalizations” might be less descriptive than “models of constrained optimization.” This book surveys models that optimize something, subject to constraints. The simplest such models are linear, and the ideas used to analyze linear models generalize easily. Over the past half century, dozens of excellent books have appeared on this subject. Why another? This book fuses five components: • It uses examples to introduce general ideas. • It engages the student in spreadsheet computation. • It surveys the uses of constrained optimization. • It presents the mathematics that relates to constrained optimization. • It links the subject to economic reasoning. Each of these components can be found in other books. Their fusion makes constrained optimization more accessible and more valuable. It stimulates the student’s interest, it quickens the learning process, it helps students to achieve mastery, and it prepares them to make effective use of the material. A well-designed example provides context. It can illustrate the applicability of the model. It can reveal a concept that holds in general. It can introduce the notation that will be needed for a more general discussion. Examples mesh naturally with spreadsheet computation. To compute on a spreadsheet is to learn interactively – the spreadsheet gives instant feedback. Spreadsheet computation also takes advantage of the re
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