John N. Hooker

Integrated Methods for Optimization

• Broschiertes Buch

Produktbeschreibung

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 givennumerous 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.

Produktdetails

• International Series in Operations Research & Management Science 100
• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 978-1-4419-4258-6
• Softcover reprint of hardcover 1st ed. 2007
• Seitenzahl: 504
• Erscheinungstermin: 23. November 2010
• Englisch
• Abmessung: 235mm x 155mm x 28mm
• Gewicht: 755g
• ISBN-13: 9781441942586
• ISBN-10: 1441942580
• Artikelnr.: 32110401

Autorenporträt

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

Inhaltsangabe

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.

Rezensionen

From the reviews of the second edition: "The book amply illustrates the power of combining the strengths of constraint programming, mathematical programming, global optimization, and heuristics by detailing these approaches, by articulating the commonalties among them, and by applying them to a wide range of optimization problems. ... this book covers the field both broadly and with sufficient granularity to provide working software engineers and graduate students with both a firm theoretical grasp of optimization and the software techniques needed to solve real-world problems. I recommend the book without reservation." (Marlin Thomas, ACM Computing Reviews, July, 2012)