SWORD v0.2 – Module-based SAT Solving
نویسندگان
چکیده
In this paper, we present SWORD – a SAT like solver that facilitates word level information. The main idea behind SWORD is based on the following observation: Current SAT solvers perform very well on instances with a large number of logic operations. But when more complex functions like arithmetic units are considered, the performance degrades with increasing data-path width. In contrast, pure word level approaches handle e.g. arithmetic operations very fast but suffer from complexity problems when irregularities in the word level structure (e.g. bit slicing) occur. SWORD tries to combine the best of both worlds: Logic operations like (bv)and, (bv)or, and (bv)xor are represented in terms of clauses while more complex functions like arithmetic operations or shifts are represented by so called modules. These modules inherit a problem specific decision as well as a problem specific propagation strategy, which is exploited during the search. Thus, SWORD combines the advantages of a Boolean proof procedure with the power of word level knowledge. Moreover, SWORD is not limited to pre-defined encodings as CNF or QF BV logic. Problem specific modules for respective domains can be developed.
منابع مشابه
SWORD - Module-based SAT Solving
In this paper, we describe SWORD – a decision procedure for bit-vector logic that uses SAT techniques and exploits word level information [6]. The main idea of SWORD is based on the following observation: While current SAT solvers perform very well on instances with a large number of logic operations, their performance on arithmetic operations degrades with increasing data-path width. In contra...
متن کاملExtending Sat Solver with Parity Constraints
Current methods for solving Boolean satisfiability problem (SAT) are scalable enough to solve discrete nonlinear problems involving hundreds of thousands of variables. However, modern SAT solvers scale poorly with problems involving parity constraints (linear equations modulo 2). Gaussian elimination can be used to solve a system of linear equation effectively but it cannot be applied as such w...
متن کاملLearning Hard Constraints in Max-SAT⋆
Solving over-constrained problems with Max-SAT solvers typically consists of finding an assignment that satisfies all the hard constraints and the maximum number of soft constraints. Despite the relevance of clause learning in SAT for solving structured instances, this technology has not yet been extended to Max-SAT. In this paper, we have incorporated a module that learns hard clauses in a bra...
متن کاملConflict-Driven XOR-Clause Learning
Modern conflict-driven clause learning (CDCL) SAT solvers are very good in solving conjunctive normal form (CNF) formulas. However, some application problems involve lots of parity (xor) constraints which are not necessarily efficiently handled if translated into CNF. This paper studies solving CNF formulas augmented with xor-clauses in the DPLL(XOR) framework where a CDCL SAT solver is coupled...
متن کاملFoundations of Hierarchical SAT-Solving
The theory of hierarchical Boolean satisfiability (SAT) solving proposed in this paper is based on a strict axiomatic system and introduces a new important notion of implicativity. The theory makes evident that increasing implicativity is the core of SAT-solving. We provide a theoretical basis for increasing the implicativity of a given SAT instance and for organizing SAT-solving in a hierarchi...
متن کامل