A logic view of 0-1 integer programming problems, providing new insights into the structure of problems that can lead the researcher to more effective solution techniques depending on the problem class. Operations research techniques are integrated into a logic programming environment. The first monographic treatment that begins to unify these two methodological approaches. 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.

Inhalt

Foreword. Preface. 1. Introduction. 2. Constraint Logic Programming. 3. Pseudo-Boolean Constraints. 4. A Logic Cut Based Constraint Solver. 5. Pseudo-Boolean Unit Resolution. 6. Logic Cuts and Enumeration. 7. Linear Pseudo-Boolean Inequalities and Extended Clauses. 8. Simplification. 9. Linearization. 10. Projection. 11. Conclusion. References.