Efficient Encodings of Conditional Cardinality Constraints
This work solves a technical bottleneck in SAT solving for applications like pattern mining, but it is incremental as it builds on existing encodings.
The paper addresses the loss of generalized arc consistency in SAT encodings when cardinality constraints are made conditional, and proposes extensions to recover this property, showing improved performance in a SAT-based pattern mining application.
In the encoding of many real-world problems to propositional satisfiability, the cardinality constraint is a recurrent constraint that needs to be managed effectively. Several efficient encodings have been proposed while missing that such a constraint can be involved in a more general propositional formulation. To avoid combinatorial explosion, Tseitin principle usually used to translate such general propositional formula to Conjunctive Normal Form (CNF), introduces fresh propositional variables to represent sub-formulas and/or complex contraints. Thanks to Plaisted and Greenbaum improvement, the polarity of the sub-formula $Φ$ is taken into account leading to conditional constraints of the form $y\rightarrow Φ$, or $Φ\rightarrow y$, where $y$ is a fresh propositional variable. In the case where $Φ$ represents a cardinality constraint, such translation leads to conditional cardinality constraints subject of the present paper. We first show that when all the clauses encoding the cardinality constraint are augmented with an additional new variable, most of the well-known encodings cease to maintain the generalized arc consistency property. Then, we consider some of these encodings and show how they can be extended to recover such important property. An experimental validation is conducted on a SAT-based pattern mining application, where such conditional cardinality constraints is a cornerstone, showing the relevance of our proposed approach.