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

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

Spis treści:
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.
References.
Index.

Nota biograficzna:
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.

Okładka tylna:
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