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130626 ||| eng |
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|a 9780387382746
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|a Hooker, John N.
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|a Integrated Methods for Optimization
|h Elektronische Ressource
|c by John N. Hooker
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|a 1st ed. 2007
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|a New York, NY
|b Springer US
|c 2007, 2007
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|a XIV, 486 p. 72 illus
|b online resource
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|a 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.
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|a Business
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|a Operations research
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|a Optimization
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|a Mathematics of Computing
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|a Computer science / Mathematics
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|a Management science
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|a Artificial Intelligence
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|a Business and Management
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|a Artificial intelligence
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|a Mathematical Modeling and Industrial Mathematics
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|a Mathematical optimization
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|a Operations Research and Decision Theory
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| 653 |
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|a Mathematical models
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|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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|a International Series in Operations Research & Management Science
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| 028 |
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|a 10.1007/978-0-387-38274-6
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|u https://doi.org/10.1007/978-0-387-38274-6?nosfx=y
|x Verlag
|3 Volltext
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|a 519.6
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|a 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
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