On the CNF encoding of cardinality constraints and beyond

BoolVar/pb v1.0 is an open source java library dedicated to the translation of pseudo-Boolean constraints into cnf formulae. Input constraints can be categorized with tags. Several encoding schemes are implemented in a way that each input constraint can be translated using one or several encoders, according to the related tags. The library can be easily extended by adding new encoders and / or new output formats. It is available at http://boolvar.sourceforge.net/.

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 formula. To avoid combinatorial explosion, the 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 → Φ, or Φ → 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 are a cornerstone, showing the relevance of our proposed approach.

2020

A translation is proposed of conjunctions of literals of the forms x = y \ z, x = y \ z, and x ∈ y, where x, y, z stand for variables ranging over the von Neumann universe of sets, into unquantified Boolean formulae of a rather simple conjunctive normal form. The formulae in the target language involve variables ranging over a Boolean field of sets, along with a difference operator and relators designating equality, nondisjointness and inclusion. Moreover, the result of each translation is a conjunction of literals of the forms x = y \ z, x = y \ z and of implications whose antecedents are isolated literals and whose consequents are either inclusions (strict or non-strict) between variables, or equalities between variables. Besides reflecting a simple and natural semantics, which ensures satisfiability-preservation, the proposed translation has quadratic algorithmic time-complexity, and bridges two languages both of which are known to have an NP-complete satisfiability problem.

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 zChaff 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.

Mathematical Programming, 1999

A mathematical programming model may contain qualitative as well as quantitative elements. One may, for example, wish to combine a rule base with numerical constraints. This raises the issue of how to represent logical constraints in inequality form so that they have a useful linear relaxation. We provide a simple recursive procedure that generates a convex hull description of any logical condition that can be written as a "cardinality rule", which seems to be a form that occurs often in practice. A cardinality rule asserts that if at least k of the propositions A 1 , . . . , A m are true, then at least of the propositions B 1 , . . . , B n are true. The main result of the paper is that the procedure in fact provides a convex hull description.

In Proceedings of the International Workshop on …, 2001

We investigate cardinality constraints of the form M,! K, where M is a set and is one of the comparison operators\=,", or\": a model of such a constraint is required to contain\ exactly, at most", or\ at least", respectively, K elements of M. Applications dealing with ...

Lecture Notes in Computer Science, 2009

We introduce Cardinality Networks, a new CNF encoding of cardinality constraints. It improves upon the previously existing encodings such as the sorting networks of in that it requires much less clauses and auxiliary variables, while arc consistency is still preserved: e.g., for a constraint x1 + . . . + xn ≤ k, as soon as k variables among the xi's become true, unit propagation sets all other xi's to false. Our encoding also still admits incremental strengthening: this constraint for any smaller k is obtained without adding any new clauses, by setting a single variable to false.

We present an approach to propagation-based SAT encoding of combinatorial problems, Boolean equi-propagation, where constraints are modeled as Boolean functions which propagate information about equalities between Boolean literals. This information is then applied to simplify the CNF encoding of the constraints. A key factor is that considering only a small fragment of a constraint model at one time enables us to apply stronger, and even complete, reasoning to detect equivalent literals in that fragment. Once detected, equivalences apply to simplify the entire constraint model and facilitate further reasoning on other fragments. Equi-propagation in combination with partial evaluation and constraint simplification provide the foundation for a powerful approach to SAT-based finite domain constraint solving. We introduce a tool called BEE (Ben-Gurion Equi-propagation Encoder) based on these ideas and demonstrate for a variety of benchmarks that our approach leads to a considerable reduction in the size of CNF encodings and subsequent speed-ups in SAT solving times.

Lecture Notes in Computer Science, 2011

We present an approach to propagation based solving, Boolean equi-propagation, where constraints are modelled as propagators of information about equalities between Boolean literals. Propagation based solving applies this information as a form of partial evaluation resulting in optimized SAT encodings. We demonstrate for a variety of benchmarks that our approach results in smaller CNF encodings and leads to speed-ups in solving times.

CoLogNet Publications, 2002

Many constraint satisfaction problems (csp's) are formulated with 0/1 variables. Sometimes this is a natural encoding, sometimes it is as a result of a reformulation of the problem, other times 0/1 variables make up only a part of the problem. Frequently we have constraints that restrict the sum of the values of variables. This can be encoded as a simple summation of the variables. However, since variables can only take 0/1 values we can also use an occurrence constraint, e.g. the number of occurrences of 1 must be k. Would this make a difference? Similarly, problems may use channelling constraints and encode these as a biconditional such as P ↔ Q (i.e. P if and only if Q). This can also be encoded in a number of ways. Might this make a difference as well? We attempt to answer these questions, using a variety of problems and two constraint programming toolkits. We show that even minor changes to the formulation of a constraint can have a profound effect on the run time of a constraint program and that these effects are not consistent across constraint programming toolkits. This leads us to a cautionary note for constraint programmers: take note of how you encode constraints, and don't assume computational behaviour is toolkit independent.