On the Effect of Learned Clauses on Stochastic Local Search

This contains supplementary data for "On the Effect of Learned Clauses on Stochastic
Local Search".

There are two competing paradigms in successful SAT solvers:
Conflict-driven clause learning (CDCL) and stochastic local search (SLS).
CDCL uses systematic exploration of the search space and has the ability
to learn new clauses. SLS examines the neighborhood of the current
complete assignment. Unlike CDCL, it lacks the ability to learn from its
mistakes. This work revolves around the question whether it is beneficial
for SLS to add new clauses to the original formula. We experimentally
demonstrate that clauses with a large number of correct literals w. r. t.
a fixed solution are beneficial to the runtime of SLS. We call such clauses
high-quality clauses.

Empirical evaluations show that short clauses learned by CDCL possess
the high-quality attribute. We study several domains of randomly generated
instances and deduce the most advantageous strategies to add high-quality
clauses as a preprocessing step. The strategies are implemented in an SLS
solver, and it is shown that this considerably improves the state-of-the-art
on randomly generated instances. The results are statistically significant.