Logic-Based Methods for Optimization: Combining Optimization and Constraint Satisfaction By John Hooker(auth.)
2000 | 505 Pages | ISBN: 0471385212 | PDF | 10 MB
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 moreContent: Chapter 1 Introduction (pages 1-14): Chapter 2 Some Examples (pages 15-42): Chapter 3 The Logic of Propositions (pages 43-60): Chapter 4 The Logic of Discrete Variables (pages 61-68): Chapter 5 The Logic of 0?1 Inequalities (pages 69-88): Chapter 6 Cardinality Clauses (pages 89-103): Chapter 7 Classical Boolean Methods (pages 105-125): Chapter 8 Logic?Based Modeling (pages 127-148): Chapter 9 Logic?Based Branch and Bound (pages 149-161): Chapter 10 Constraint Generation (pages 163-183): Chapter 11 Domain Reduction (pages 185-202): Chapter 12 Constraint Programming (pages 203-223): Chapter 13 Continuous Relaxations (pages 225-270): Chapter 14 Decomposition Methods (pages 271-284): Chapter 15 Branching Rules (pages 285-304): Chapter 16 Relaxation Duality (pages 305-323): Chapter 17 Inference Duality (pages 325-360): Chapter 18 Search Strategies (pages 361-388): Chapter 19 Logic?Based Benders Decomposition (pages 389-422): Chapter 20 Nonserial Dynamic Programming (pages 423-441): Chapter 21 Discrete Relaxations (pages 443-462):

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