On using satisfiability-based pruning techniques in covering algorithms

Covering problems are widely used as a modeling tool in Electronic Design Automation (EDA). Recent years have seen dramatic improvements in algorithms for the Unate/Binate Covering Problem (UCP/BCP). Despite these improvements, BCP is a well-known computationally hard problem, with many existing real-world instances that currently are hard or even impossible to solve. In this paper we apply search pruning techniques from the Boolean Satisfiability (SAT) domain to BCP. Furthermore, we generalize these techniques, in particular the ability to backtrack non-chronologically to exploit the actual formulation of covering problems. Experimental results, obtained on representative instances of the unate and binate covering problems, indicate that the proposed techniques provide significant performance gains for different classes of instances.