Strong local consistency algorithms for table constraints

Abstract

Table constraints are important in constraint programming as they are present in many real problems from areas such as configuration and databases. As a result, numerous specialized algorithms that achieve generalized arc consistency (GAC) on table constraints have been proposed. Since these algorithms achieve GAC, they operate on one constraint at a time. In this paper we propose new filtering algorithms for positive table constraints that achieve stronger local consistency properties than GAC by exploiting intersections between constraints. The first algorithm, called maxRPWC+, is a domain filtering algorithm that is based on the local consistency maxRPWC and extends the GAC algorithm of Lecoutre and Szymanek [23]. The second algorithm extends the state-of-the-art STR-based algorithms to stronger relation filtering consistencies, i.e., consistencies that can remove tuples from constraints' relations. Experimental results from benchmark problems demonstrate that the proposed algorithms are quite competitive with standard GAC algorithms like STR2 in some classes of problems with intersecting table constraints, being orders of magnitude faster in some cases.

Improving Relational Consistency Algorithms Using Dynamic Relation Partitioning

“…In recent years, new techniques for enforcing higher levels of consistency have been proposed. While most consider combinations of two constraints [3,[20][21][22], some operate on combinations of two or more constraints [2,6,15,16,23,24]. In this paper, we improve the performance of the algorithm for enforcing the relational consistency property R( * ,m)C [15,16] (originally known as m-wise consistency [10]).…”