On Improving Local Search for Unsatisfiability
  Stochastic local search (SLS) has been an active field of research in the
last few years, with new techniques and procedures being developed at an
astonishing rate. SLS has been traditionally associated with satisfiability
solving, that is, finding a solution for a given problem instance, as its
intrinsic nature does not address unsatisfiable problems. Unsatisfiable
instances were therefore commonly solved using backtrack search solvers. For
this reason, in the late 90s Selman, Kautz and McAllester proposed a challenge
to use local search instead to prove unsatisfiability. More recently, two SLS
solvers - Ranger and Gunsat - have been developed, which are able to prove
unsatisfiability albeit being SLS solvers. In this paper, we first compare
Ranger with Gunsat and then propose to improve Ranger performance using some of
Gunsat's techniques, namely unit propagation look-ahead and extended
resolution.
