Hooker Logic-Based Methods for Optimization

Combining Optimization and Constraint Satisfaction
1. Auflage 2011
ISBN: 978-1-118-03128-5
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 520 Seiten, E-Book

Reihe: Wiley-Interscience Series in Discrete Mathematics and Optimization

A pioneering look at the fundamental role of logic in optimizationand constraint satisfaction
While recent efforts to combine optimization and constraintsatisfaction have received considerable attention, little has beensaid about using logic in optimization as the key to unifying thetwo fields. Logic-Based Methods for Optimization develops for thefirst time a comprehensive conceptual framework for integratingoptimization and constraint satisfaction, then goes a step furtherand shows how extending logical inference to optimization allowsfor more powerful as well as flexible modeling and solutiontechniques. Designed to be easily accessible to industryprofessionals and academics in both operations research andartificial intelligence, the book provides a wealth of examples aswell as elegant techniques and modeling frameworks ready forimplementation. Timely, original, and thought-provoking,Logic-Based Methods for Optimization:
* Demonstrates the advantages of combining the techniques inproblem solving
* Offers tutorials in constraint satisfaction/constraintprogramming and logical inference
* Clearly explains such concepts as relaxation, cutting planes,nonserial dynamic programming, and Bender's decomposition
* Reviews the necessary technologies for software developersseeking to combine the two techniques
* Features extensive references to important computationalstudies
* And much more

Autoren/Hrsg.

Hooker, John

Inhaltsverzeichnis

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.

Über Autor(innen)

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.