4 research outputs found
On Tackling the Limits of Resolution in SAT Solving
The practical success of Boolean Satisfiability (SAT) solvers stems from the
CDCL (Conflict-Driven Clause Learning) approach to SAT solving. However, from a
propositional proof complexity perspective, CDCL is no more powerful than the
resolution proof system, for which many hard examples exist. This paper
proposes a new problem transformation, which enables reducing the decision
problem for formulas in conjunctive normal form (CNF) to the problem of solving
maximum satisfiability over Horn formulas. Given the new transformation, the
paper proves a polynomial bound on the number of MaxSAT resolution steps for
pigeonhole formulas. This result is in clear contrast with earlier results on
the length of proofs of MaxSAT resolution for pigeonhole formulas. The paper
also establishes the same polynomial bound in the case of modern core-guided
MaxSAT solvers. Experimental results, obtained on CNF formulas known to be hard
for CDCL SAT solvers, show that these can be efficiently solved with modern
MaxSAT solvers
Word-level Symbolic Trajectory Evaluation
Symbolic trajectory evaluation (STE) is a model checking technique that has
been successfully used to verify industrial designs. Existing implementations
of STE, however, reason at the level of bits, allowing signals to take values
in {0, 1, X}. This limits the amount of abstraction that can be achieved, and
presents inherent limitations to scaling. The main contribution of this paper
is to show how much more abstract lattices can be derived automatically from
RTL descriptions, and how a model checker for the general theory of STE
instantiated with such abstract lattices can be implemented in practice. This
gives us the first practical word-level STE engine, called STEWord. Experiments
on a set of designs similar to those used in industry show that STEWord scales
better than word-level BMC and also bit-level STE.Comment: 19 pages, 3 figures, 2 tables, full version of paper in International
Conference on Computer-Aided Verification (CAV) 201
A new SAT-based algorithm for symbolic trajectory evaluation
Abstract. We present a new SAT-based algorithm for Symbolic Trajectory Evaluation (STE), and compare it to more established SAT-based techniques for STE.