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Logic-Based Methods for Optimization: - Hardcover, by Hooker John - Good
US $204.93
ApproximatelyRM 874.85
Condition:
Good
A book that has been read but is in good condition. Very minimal damage to the cover including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. Binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, no writing in margins. No missing pages.
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Located in: Philadelphia, Pennsylvania, United States
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eBay item number:127094701660
Item specifics
- Condition
- Book Title
- Logic-Based Methods for Optimization: Combining Optimization and
- ISBN
- 9780471385219
About this product
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0471385212
ISBN-13
9780471385219
eBay Product ID (ePID)
1653333
Product Key Features
Number of Pages
520 Pages
Language
English
Publication Name
Logic-Based Methods for Optimization : Combining Optimization and Constraint Satisfaction
Publication Year
2000
Subject
Linear & Nonlinear Programming, Logic, Optimization, Discrete Mathematics
Type
Textbook
Subject Area
Mathematics
Series
Wiley Series in Discrete Mathematics and Optimization Ser.
Format
Hardcover
Dimensions
Item Height
1.1 in
Item Weight
31.8 Oz
Item Length
9.6 in
Item Width
6.4 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
99-088732
Reviews
"This is a book that should be widely read by graduate students and researchers in both the computer science and optimization communities." (Choice, Vol. 38, No. 7, March 2001) "Goal is to broaden the conceptual foundations of optimization to include logical and constraint based approaches to traditional optimization methods." (American Mathematical Monthly, November 2001) "The author combines a low-key, often conversational presentation with enthusiasm for a synthesis with traditional optimization methods..." (SIAM Review, Vol. 43, No. 4) "The book is for practitioners as well as theorists" (Zentralblatt Math, Vol.974, No.24, 2001), "...book...should be widely read by graduate students and researchers in both the computer science and optimization communities." (Choice, Vol. 38, No. 7, March 2001) "Goal is to broaden the conceptual foundations of optimization to include logical and constraint based approaches to traditional optimization methods." (American Mathematical Monthly, November 2001) "The author combines a low-key, often conversational presentation with enthusiasm for a synthesis with traditional optimization methods..." (SIAM Review, Vol. 43, No. 4) "The book is for practitioners as well as theorists" (Zentralblatt Math, Vol.974, No.24, 2001)
Dewey Edition
21
Series Volume Number
2
Illustrated
Yes
Dewey Decimal
519.7/2
Table Of Content
Some Examples. The Logic of Propositions. The Logic of Discrete Variables. The Logic of 0-1 Inequalities. Cardinality Clauses. Classical Boolean Methods. Logic-Based Modeling. Logic-Based Branch and Bound. Constraint Generation. Domain Reduction. Constraint Programming. Continuous Relaxations. Decomposition Methods. Branching Rules. Relaxation Duality. Inference Duality. Search Strategies. Logic-Based Benders Decomposition. Nonserial Dynamic Programming. Discrete Relaxations. References. Index.
Synopsis
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 Benders decomposition Reviews the necessary technologies for software developers seeking to combine the two techniques Features extensive references to important computational studies And much more, Dieser Band untersucht die Rolle der Logik bei der Optimierung - Grundlage für eine neue Generation von Vorlesungen in diskreter Optimierung! In ausgesprochen klarem, technisch sauberen Stil werden Tools und Techniken erläutert sowie vielfältige Programmieroptionen (ILOG, CPLEX, OPL, AMPL, GAMS und andere). Mit zahlreichen anschaulichen Beispielen. (07/00), 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 more, 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.
LC Classification Number
T57.74.H66 2000
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Seller feedback (174,858)
- u***n (771)- Feedback left by buyer.Past monthVerified purchaseThanks for the quick shipping!
- a***c (1001)- Feedback left by buyer.Past monthVerified purchaseLove it, Thank you!!!
- n***6 (197)- Feedback left by buyer.Past monthVerified purchasearrived as described in great shape fast shipper