High-Level Reformulation of Constraint Programs
نویسندگان
چکیده
We propose a set of reformulation rules for models of constraint satisfaction problems that are written in our high-level constraint programming language ESRA, which is more expressive than OPL and is compiled into OPL. These automatable rules achieve models that are often very similar to what a human modeller would have tried, such as switching from a pure constraint program to an integer linear program. Since, for a given solver and a given instance of a problem, it is very hard to figure out which model is the best, we advocate that tool support of our reformulation rules should operate with a set of training instances. Indeed, this is the only way of guaranteeing that the actually chosen reformulation pays off, at least for instances within the distribution underlying the training instances. RÉSUMÉ. Nous proposons un jeu de règles de reformulation pour modèles de problèmes de satisfaction de contraintes exprimés dans notre langage de haut niveau ESRA pour programmation par contraintes, qui est plus expressif qu’OPL et qui est compilé en OPL. Ces règles automatisables donnent des modèles souvent très similaires à ce qu’un modélisateur humain aurait essayé, comme par exemple le passage d’un pur programme par contraintes à un programme linéaire entier. Comme, pour un solveur donné et une instance donnée d’un problème, il est très difficile de déterminer quel modèle est le meilleur, nous maintenons qu’un outil supportant nos règles de reformulation devrait utiliser un jeu d’instances d’entraînement. En effet, ceci est la seule voie pour garantir que la reformulation effectivement choisie soit rentable, du moins pour les instances dans la distribution sousjacente aux instances d’entraînement.
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تاریخ انتشار 2001