Efficient SAT Solving under Assumptions

Abstract

In incremental SAT solving, assumptions are propositions that hold solely for one specific invocation of the solver. Effective propagation of assumptions is vital for ensuring SAT solving efficiency in a variety of applications. We propose algorithms to handle assumptions. In our approach, assumptions are modeled as unit clauses, in contrast to the current state-of-the-art approach that models assumptions as first decision variables. We show that a notable advantage of our approach is that it can make preprocessing algorithms much more effective. However, our initial scheme renders assumption-dependent (or temporary) conflict clauses unusable in subsequent invocations. To resolve the resulting problem of reduced learning power, we introduce an algorithm that transforms such temporary clauses into assumption-independent pervasive clauses. In addition, we show that our approach can be enhanced further when a limited form of look-ahead information is available. We demonstrate that our approach results in a considerable performance boost of the SAT solver on instances generated by a prominent industrial application in hardware validation.