+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)


Logic-Based Methods for Optimization. Combining Optimization and Constraint Satisfaction. Edition No. 1. Wiley Series in Discrete Mathematics and Optimization

  • ID: 2172702
  • Book
  • June 2000
  • 520 Pages
  • John Wiley and Sons Ltd
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
Note: Product cover images may vary from those shown
Some Examples.

The Logic of Propositions.

The Logic of Discrete Variables.

The Logic of 0-1 Inequalities.

Cardinality Clauses.

Classical Boolean Methods.

Logic-Based Modeling.

Logic-Based Branch and Bound.

Constraint Generation.

Domain Reduction.

Constraint Programming.

Continuous Relaxations.

Decomposition Methods.

Branching Rules.

Relaxation Duality.

Inference Duality.

Search Strategies.

Logic-Based Benders Decomposition.

Nonserial Dynamic Programming.

Discrete Relaxations.


Note: Product cover images may vary from those shown
John Hooker Carnegie Mellon University.
Note: Product cover images may vary from those shown