Integrated Methods For Optimization / Edition 1

by John N. Hooker

Overview

Integrated Methods for Optimization integrates the key concepts of Mathematical Programming and Constraint Programming into a unified framework that allows them to be generalized and combined. The unification of MP and CP creates optimization methods that have much greater modeling power, increased computational speed, and a sizeable reduction computational coding. Hence the benefits of this integration are substantial, providing the Applied Sciences with a powerful, high-level modeling solution for optimization problems. As reviewers of the book have noted, this integration along with constraint programming being incorporated into a number of programming languages, brings the field a step closer to being able to simply state a problem and having the computer solve it.

John Hooker is a leading researcher in both the Optimization and Constraint Programming research communities. He has been an instrumental principal for this integration, and over the years, he has given numerous presentations and tutorials on the integration of these two areas. It is felt by many in the field that the future Optimization courses will increasingly be taught from this integrated framework.

Product Details

ISBN-13:	9780387382722
Publisher:	Springer-Verlag New York, LLC
Publication date:	07/27/2010
Series:	International Series in Operations Research & Management Science Series , #100
Edition description:	New Edition
Pages:	506
Product dimensions:	1.13(w) x 9.21(h) x 6.14(d)

About the Author

John Hooker is a leading researcher in both the Optimization and Constraint Programming research communities. He has been an instrumental principal for this integration, and over the years, he has given numerous presentations and tutorials on the integration of these two areas. It is felt by many in the field that the future Optimization courses will increasingly be taught from this integrated framework.

Prof. Hooker has published two earlier books on the methodologies of Optimization and Constraint Programming. The first was Optimization Methods for Logical Inference (Wiley 1999) and the second was Logic Based Methods for Optimization: Combining Optimization and Constraints Satisfaction (Wiley 2000). This book will be his third book in this evolving area and it is the book that completes the process of integrating these two methodologies into a single set of methods

Table of Contents

Preface.- Introduction.- Search.- The solution process.- Branching search.- Constraint-directed search.- Local search.- Bibliographic notes.- Inference.- Completeness.- Inference duality.- Linear inequalities.- General inequality constraints.- Propositional logic.- 0-1 linear inequalities.- Integer linear inequalities.- The element constraint.- The all-different constraint.- The cardinality and Nvalues constraints.- The circuit constraint.- The stretch constraint.- Disjunctive scheduling.- Cumulative scheduling.- Bibliographic notes.- Relaxation.- Relaxation duality.- Linear inequalities.- Semicontinuous piecewise linear functions.- 0-1 linear inequalities.- Integer linear inequalities.- Lagrangean and surrogate relaxations.- Disjunctions of linear systems.- Disjunctions of nonlinear systems.- MILP modeling.- Propositional Logic.- The element constraint.- The all-different constraint.- The cardinality constraint.- The circuit constraint.- Disjunctive scheduling.- Cumulative scheduling.- Bibliographic notes.- Dictionary of constraints.- References.- Index.