An efficient heuristic approach combining maximal itemsets and area measure for compressing voluminous table constraints

TL;DR

This paper introduces a novel heuristic combining maximal itemsets and area measure to efficiently compress large table constraints in constraint programming, improving space and time efficiency.

Contribution

It presents a new approach using maximal frequent itemsets and area measure for compressing table constraints, enhancing existing methods.

Findings

Effective compression of voluminous table constraints

Improved solving efficiency on compressed constraints

Demonstrated scalability and effectiveness through experiments

Abstract

Constraint Programming is a powerful paradigm to model and solve combinatorial problems. While there are many kinds of constraints, the table constraint is perhaps the most significant-being the most well-studied and has the ability to encode any other constraints defined on finite variables. However, constraints can be very voluminous and their size can grow exponentially with their arity. To reduce space and the time complexity, researchers have focused on various forms of compression. In this paper we propose a new approach based on maximal frequent itemsets technique and area measure for enumerating the maximal frequent itemsets relevant for compressing table constraints. Our experimental results show the effectiveness and efficiency of this approach on compression and on solving compressed table constraints.