Before a combinatorial problem can be solved by current SAT methods, it must usually be
encoded in conjunctive normal form, which facilitates algorithm implementation and allows a …

One of the most important paradigm shifts in the use of SAT solvers for solving industrial
problems has been the introduction of clause learning. Clause learning entails adding a …

In this paper, we describe and evaluate three different techniques for translating pseudo-
boolean constraints (linear constraints over boolean variables) into clauses that can be …

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 …

Finite domain propagation solvers effectively represent the possible values of variables by a
set of choices which can be naturally modelled as Boolean variables. In this paper we …

In this paper, we propose a new method to encode Constraint Satisfaction Problems (CSP)
and Constraint Optimization Problems (COP) with integer linear constraints into Boolean …

Motivated by the performance improvements made to SAT solvers in recent years, a number
of different encodings of constraints into SAT have been proposed. Concrete examples are …

When solving a combinatorial problem using Constraint Programming (CP) or Satisfiability
(SAT), modelling and formulation are vital and difficult tasks. Even an expert human may …

Satisfiability (SAT) solvers have been using the same input format for decades: a formula in
conjunctive normal form. Cardinality constraints appear frequently in problem descriptions …

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 …