Logic-Based 0–1 Constraint Programming: Operations Research/Computer Science Interfaces Series, cartea 5
Autor Peter Barthen Limba Engleză Hardback – 30 noi 1995
Logic-based methods for modelling and solving combinatorial problems have recently started to play a significant role in both theory and practice. The application of logic to combinatorial problems has a dual aspect. On one hand, constraint logic programming allows one to declaratively model combinatorial problems over an appropriate constraint domain, the problems then being solved by a corresponding constraint solver. Besides being a high-level declarative interface to the constraint solver, the logic programming language allows one also to implement those subproblems that cannot be naturally expressed with constraints. On the other hand, logic-based methods can be used as a constraint solving technique within a constraint solver for combinatorial problems modelled as 0-1 integer programs.
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Specificații
ISBN-13: 9780792396635
ISBN-10: 0792396634
Pagini: 254
Ilustrații: XIV, 254 p.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.56 kg
Ediția:1996
Editura: Springer Us
Colecția Springer
Seria Operations Research/Computer Science Interfaces Series
Locul publicării:New York, NY, United States
ISBN-10: 0792396634
Pagini: 254
Ilustrații: XIV, 254 p.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.56 kg
Ediția:1996
Editura: Springer Us
Colecția Springer
Seria Operations Research/Computer Science Interfaces Series
Locul publicării:New York, NY, United States
Public țintă
ResearchCuprins
1 Introduction.- 1.1 CLP and Combinatorial Optimization.- 1.2 Logic-based Pseudo-Boolean Constraint Solving.- 2 Constraint Logic Programming.- 2.1 CLP by Example.- 2.2 Semantics.- 2.3 Constraint Solving.- 2.4 Local Consistency and CLP.- 2.5 Logic Programming with Pseudo-Boolean Constraints.- 2.6 Existing CLP-systems for combinatorial optimization.- 3 Pseudo-Boolean Constraints.- 3.1 Preliminaries.- 3.2 Previous Work.- 3.3 Generalized Resolution.- 4 A Logic Cut Based Constraint Solver.- 4.1 The Solved Form.- 4.2 Reaching the Solved Form.- 5 Pseudo-Boolean Unit Resolution.- 5.1 The Classical Davis-Putnam Procedure.- 5.2 Davis-Putnam for Linear Pseudo-Boolean Inequalities.- 5.3 Optimizing with Pseudo-Boolean Davis-Putnam.- 5.4 Implementation.- 5.5 Heuristics.- 5.6 Computational Results.- 5.7 Pseudo-Boolean Davis-Putnam for CLP.- 6 Logic Cuts and Enumeration.- 6.1 Valid Extended Clauses by Enumeration.- 6.2 A Pure Logic Cut Algorithm.- 6.3 Entailment and Logic Cuts.- 7 Linear Pseudo-Boolean Inequalities and Extended Clauses.- 7.1 Generating Valid Extended Clauses.- 7.2 Identifying Redundant Extended Clauses.- 7.3 Implementation.- 7.4 Symmetries.- 7.5 Detection of Fixed Literals.- 7.6 Constrained Simplification.- 7.7 Cut Generation for 0–1 Integer Programming.- 7.8 Implementing Resolution.- 8 Simplification.- 8.1 An Order for Extended Clauses.- 8.2 Simplifying Diagonal Sums.- 8.3 Simplifying Resolvents.- 8.4 Equations between Literals.- 9 Linearization.- 9.1 Previous Work.- 9.2 A Linearization Method.- 9.3 Deriving Lower Bounds.- 9.4 Further Improvements.- 10 Projection.- 10.1 Projection for Prime Extended Clauses.- 10.2 Projection for Extended Clauses.- 10.3 Improvements.- 11 Conclusion.- 11.1 Summary of Results.- 11.2 Related and Future Research.- References.