Theory of linear and integer programming
Theory of linear and integer programming
POPL '87 Proceedings of the 14th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
A methodology for managing hard constraints in CLP systems
PLDI '91 Proceedings of the ACM SIGPLAN 1991 conference on Programming language design and implementation
The CLP( R ) language and system
ACM Transactions on Programming Languages and Systems (TOPLAS)
An abstract machine for CLP(R)
PLDI '92 Proceedings of the ACM SIGPLAN 1992 conference on Programming language design and implementation
Redundancy of variables in CLD R
ILPS '93 Proceedings of the 1993 international symposium on Logic programming
Projecting CLP( R ) constraints
Selected papers of international conference on Fifth generation computer systems 92
Redundancy, variable elimination and linear disequations
ILPS '94 Proceedings of the 1994 International Symposium on Logic programming
An Optimizing Compiler for CLP(R)
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
Variable Independence, Quantifier Elimination, and Constraint Representations
ICALP '00 Proceedings of the 27th International Colloquium on Automata, Languages and Programming
Projection in Adaptive Constraint Handling
Selected papers from the Joint ERCIM/Compulog Net Workshop on New Trends in Contraints
Constraints
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During the evaluation of a constraint logic program, many local variables become inaccessible, or dead. In Prolog and other programming languages, the data bound to local variables can be removed automatically by garbage collection. The case of CLP is more complex, as the variables may be involved in several constraints. We can consider dead variables to be existentially quantified. Removing an existential variable from a set of constraints is then a problem of quantifier elimination, or projection. Eliminating variables not only allows recovery of space but also can decrease the cost of further consistency tests. Surprisingly, the existing systems do not exploit these advantages. Instead, the primary use of projection is as a mechanism for obtaining answer constraints. In this paper, we will give a general system architecture for automatic early projection and specify the heuristics for CLP(R) together with an in-situ removal method. We then show the effectiveness of early projection by applying it to some practical planning problems.