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Logic-Based Methods for Optimization: Combining Optimization and Constraint Satisfaction

Logic-Based Methods for Optimization: Combining Optimization and Constraint Satisfaction

Logic-Based Methods for Optimization: Combining Optimization and Constraint
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Logic-Based Methods for Optimization: Combining Optimization and Constraint Satisfaction Hardback - 2000 - 1st Edition

by Hooker, John

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Wiley-Interscience. First Edition. Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Details

  • Title Logic-Based Methods for Optimization: Combining Optimization and Constraint Satisfaction
  • Author Hooker, John
  • Binding Hardback
  • Edition number 1st
  • Edition First Edition
  • Condition Used - Very good
  • Pages 520
  • Volumes 1
  • Language ENG
  • Publisher Wiley-Interscience
  • Publication date 2000-05-30
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # 0471385212-11-1
  • ISBN 9780471385219 / 0471385212
  • Weight 1.83 lbs (0.83 kg)
  • Dimensions 9.6 x 5.92 x 1.19 in (24.38 x 15.04 x 3.02 cm)
  • Category Mathematics
  • Library of Congress subjects Mathematical optimization, Logic, Symbolic and mathematical
  • Library of Congress Catalogue Number 99088732
  • Dewey Decimal Code 519.72
  • Quantity available 2

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From the publisher

Dieser Band untersucht die Rolle der Logik bei der Optimierung - Grundlage fr eine neue Generation von Vorlesungen in diskreter Optimierung! In ausgesprochen klarem, technisch sauberen Stil werden Tools und Techniken erlutert sowie vielfltige Programmieroptionen (ILOG, CPLEX, OPL, AMPL, GAMS und andere). Mit zahlreichen anschaulichen Beispielen. (07/00)

First line

Logical inference is inseparable from optimization.

From the rear cover

A pioneering look at the fundamental role of logic in optimization and constraint satisfaction

While recent efforts to combine optimization and constraint satisfaction have received considerable attention, little has been said about using logic in optimization as the key to unifying the two fields. Logic-Based Methods for Optimization develops for the first time a comprehensive conceptual framework for integrating optimization and constraint satisfaction, then goes a step further and shows how extending logical inference to optimization allows for more powerful as well as flexible modeling and solution techniques. Designed to be easily accessible to industry professionals and academics in both operations research and artificial intelligence, the book provides a wealth of examples as well as elegant techniques and modeling frameworks ready for implementation. Timely, original, and thought-provoking, Logic-Based Methods for Optimization:
* Demonstrates the advantages of combining the techniques in problem solving
* Offers tutorials in constraint satisfaction/constraint programming and logical inference
* Clearly explains such concepts as relaxation, cutting planes, nonserial dynamic programming, and Bender's decomposition
* Reviews the necessary technologies for software developers seeking to combine the two techniques
* Features extensive references to important computational studies
* And much more

Media reviews

Citations

  • Choice, 03/01/2001, Page 1304
  • Scitech Book News, 12/01/2000, Page 125

About the author

JOHN HOOKER, PhD, is Professor of Operations Research and T. Jerome Holleran Professor of Business Ethics and Social Responsibility at the Graduate School of Industrial Administration, Carnegie Mellon University. Well-known for his work in the operations research/computer science interface, Dr. Hooker has published over 80 articles and coauthored (with Vijay Chandru) Optimization Methods for Logical Inference, also available from Wiley.

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