Unate and binate covering problems are a subclass of general integer linear
programming problems with which several problems in logic synthesis, such
as two-level logic minimization and technology mapping, are formulated. Pre
vious branch-and-bound methods for solving these problems exactly use lower
bounding techniques based on finding maximal independent sets, In this pap
er, we examine lower bounding techniques based on linear programming relaxa
tion (LPR) for the covering problem. We show that a combination of traditio
nal reductions (essentiality and dominance) and incremental computation of
LPR-based lower bounds can exactly solve difficult covering problems orders
of magnitude faster than traditional methods.