0000000000290726
AUTHOR
Yacine Boufkhad
A Translation of Pseudo Boolean Constraints to SAT
Research note; This paper introduces a new CNF encoding of pseudo-Boolean constraints, which allows unit propagation to maintain generalized arc consistency. In the worst case, the size of the produced formula can be exponentially related to the size of the input constraint, but some important classes of pseudo-Boolean constraints, including Boolean cardinality constraints, are encoded in polynomial time and size. The proposed encoding was integrated in a solver based on the zCha SAT solver and submitted to the PB05 evaluation. The results provide new perspectives in the field of full CNF approach of pseudo-Boolean constraints solving.
New Encodings of Pseudo-Boolean Constraints into CNF
International audience; This paper answers affirmatively the open question of the existence of a polynomial size CNF encoding of pseudo-Boolean (PB) constraints such that generalized arc consistency (GAC) is maintained through unit propagation (UP). All previous encodings of PB constraints either did not allow UP to maintain GAC, or were of exponential size in the worst case. This paper presents an encoding that realizes both of the desired properties. From a theoretical point of view, this narrows the gap between the expressive power of clauses and the one of pseudo-Boolean constraints.
Full CNF Encoding: The Counting Constraints Case
Many problems are naturally expressed using CNF clauses and boolean cardinality constraints. It is generally believed that solving such problems through pure CNF encoding is inefficient, so many authors has proposed specialized algorithms : the pseudo-boolean solvers. In this paper we show that an appropriate pure CNF encoding can be competitive with these specialized methods. In conjunction with our encoding, we propose a slight modification of the DLL procedure that allows any DLL-based SAT solver to solve boolean cardinality optimization problems. We show experimentally that our encoding allows zchaff to be competitive with pseudo-boolean solvers on some decision and optimization problem…
Efficient CNF Encoding of Boolean Cardinality Constraints
In this paper, we address the encoding into CNF clauses of Boolean cardinality constraints that arise in many practical applications. The proposed encoding is efficient with respect to unit propagation, which is implemented in almost all complete CNF satisfiability solvers. We prove the practical efficiency of this encoding on some problems arising in discrete tomography that involve many cardinality constraints. This encoding is also used together with a trivial variable elimination in order to re-encode parity learning benchmarks so that a simple Davis and Putnam procedure can solve them.