Unsatisfying Walks: Solving False QBFs with Local Search

In the past few years, we have seen significant progress in the area of Boolean satisfiability (SAT) solving and its applications. More recently, new efforts have focused on developing solvers for Quantified Boolean Formulas (QBFs). Recent QBF e valuation results show that developing practical QBF solvers is more challenging than one might expect. Even relatively small QBF problems are sometimes beyond the reach of current QBF solvers. We present a new approach for solving unsatisfiable two-alternation QBF s. Our approach is able to solve hard random QBF formulas that other current algorithms cannot handle. Our solver WalkMinQBF combines the power of stochastic local search methods and com- plete SAT solvers. The solver is incomplete, in that it outputs unsat if a certificate for un- satisfiability is found, otherwise it outputs unknown. We test our solver on the model for random formulas introduced in (3) and the Models A and B introduced in (8). We compare WalkMinQBF with the state-of-the-art QBF solvers Ssolve and QuBE-BJ. We show that WalkMinQBF outperforms Ssolve and QuBE-BJ in time and in the number of formulas solved. We believe our work provides new heuristic insights that should be useful in complete QBF solvers. As a side result we have developed a stochastic local search algorithm for the minimum unsatisfiability problem (MIN-SAT).