Search pruning techniques in SAT-based branch-and-bound algorithms for the binate covering problem

Covering problems are widely used as a modeling tool in electronic design automation. 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 domain to branch-and-bound algorithms for BCP. Furthermore, we generalize these techniques, in particular the ability to infer and record new constraints from conflicts and the ability to backtrack nonchronologically, to situations where the branch-and-bound BCP algorithm backtracks due to bounding conditions. Experimental results, obtained on representative real-world instances of the UCP/BCP, indicate that the proposed techniques are effective and can provide significant performance gains for specific classes of instances