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Logic-Based Methods for Optimization

Logic-Based Methods for Optimization

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

by John Hooker

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Description

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

John Hooker New York: Wiley‑Interscience, 2000 First Edition ISBN: 0471385212 / 9780471385219 Hardcover

Description

John Hooker's landmark volume presents a unified framework that bridges mathematical optimization and constraint satisfaction, two fields that traditionally evolved in parallel. The book introduces logic‑based optimization, a methodology that leverages logical inference to strengthen optimization models, reduce search complexity, and integrate heterogeneous reasoning systems.

Hooker develops:

  • the theoretical foundations of logic‑based optimization, including propositional and first‑order logic as modeling tools,

  • hybrid methods that combine integer programming, constraint programming, and logical inference,

  • decomposition techniques that exploit problem structure for more efficient solving,

  • applications across scheduling, resource allocation, configuration, and combinatorial optimization,

  • comparisons between classical optimization and logic‑driven approaches, highlighting when each method excels,

This first edition remains a foundational reference for researchers working at the intersection of operations research, AI, and constraint reasoning. Its presence in a research library signals a commitment to advanced, cross‑disciplinary methods in computational problem solving.

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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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