In this paper, we describe Compact-Table (CT), a bitwise algorithm to enforce Generalized
Arc Consistency (GAC) on table constraints. Although this algorithm is the default propagator …

In this document, we introduce PyCSP $3 $, a Python library that allows us to write models of
combinatorial constrained problems in a declarative manner. Currently, with PyCSP $3 …

Table Constraints are very useful for modeling combinatorial problems in Constraint
Programming (CP). They are a universal mechanism for representing constraints, but …

Constraint Programming is a powerful paradigm to model and solve combinatorial problems.
While there are many kinds of constraints, the table constraint (also called a CSP) is perhaps …

Constraint solvers are complex pieces of software and are notoriously difficult to debug. In
large part this is due to the difficulty of pinpointing the source of an error in the vast searches …

Table constraints are very useful for modeling combinatorial constrained problems, and thus
play an important role in Constraint Programming (CP). During the last decade, many …

Abstract Maintaining Generalized Arc Consistency (GAC) during search is considered an
efficient way to solve non-binary constraint satisfaction problems. Bit-based representations …

Constraint propagation is a key to the success of Constraint Programming (CP). The
principle is that filtering algorithms associated with constraints are executed in sequence …

Table constraints are instrumental in modelling combinatorial problems with Constraint
Programming. Recently, Compact-Table (CT) has been proposed and shown to be as an …

These last years, there has been a growing interest for structures like tables and decision
diagrams in Constraint Programming (CP). This is due to the universal character of these …