XCSP3-core: A Format for Representing Constraint Satisfaction/Optimization Problems

In this document, we introduce XCSP3-core, a subset of XCSP3 that allows us to represent constraint satisfaction/optimization problems. The interest of XCSP3-core is multiple: (i) focusing on the most popular frameworks (CSP and COP) and constraints, (ii) facilitating the parsing process by means of dedicated XCSP3-core parsers written in Java and C++ (using callback functions), (iii) and defining a core format for comparisons (competitions) of constraint solvers.Comment: arXiv admin note: substantial text overlap with arXiv:1611.0339

PYCSP3: Modeling Combinatorial Constrained Problems in Python

In this document, we introduce PYCSP$3$, a Python library that allows us to write models of combinatorial constrained problems in a simple and declarative way. Currently, with PyCSP$3$, you can write models of constraint satisfaction and optimization problems. More specifically, you can build CSP (Constraint Satisfaction Problem) and COP (Constraint Optimization Problem) models. Importantly, there is a complete separation between modeling and solving phases: you write a model, you compile it (while providing some data) in order to generate an XCSP3 instance (file), and you solve that problem instance by means of a constraint solver. In this document, you will find all that you need to know about PYCSP$3$, with more than 40 illustrative models

Extending Compact-Table to Negative and Short Tables

Table constraints are very useful for modeling combinato- rial constrained problems, and thus play an important role in Constraint Programming (CP). During the last decade, many algorithms have been proposed for enforcing the property known as Generalized Arc Consistency (GAC) on such con- straints. A state-of-the art GAC algorithm called Compact- Table (CT), which has been recently proposed, significantly outperforms all previously proposed algorithms. In this pa- per, we extend this algorithm in order to deal with both short supports and negative tables, i.e., tables that contain univer- sal values and conflicts. Our experimental results show the interest of using this fast general algorithm

Extension de Compact-Table aux tables négatives et concises

Ces dernières années, plusieurs algorithmes pour la contrainte table ont été proposés pour assurer la propriété de cohérence d’arc généralisée (GAC). Compact-Table (CT) [1] est un algorithme récent de l’état-de-l’art, que nous avons étendu dans notre article [4], intitulé ”Extending Compact-Table to Negative and Short Tables” et publié à AAAI-17, aux tables concises (contenant des supports courts) et aux tables négatives (contenant des conflits)