Logic-Based Methods for Optimization: Combining Optimization and Constraint Satisfaction
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Skip to Main Content. First published: 16 May All rights reserved. About this book 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.
A pioneering look at the fundamental role of logic in optimization and constraint satisfaction. While recent efforts to combine optimization and. Buy Logic-Based Methods for Optimization: Combining Optimization and Constraint Satisfaction on giuliettasprint.konfer.eu ✓ FREE SHIPPING on qualified orders.
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.
Reviews "This is a book that should be widely read by graduate students and researchers in both the computer science and optimization communities.
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Hooker has published over 80 articles and coauthored with Vijay Chandru Optimization Methods for Logical Inference, also available from Wiley. Free Access.
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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. Passar bra ihop.
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