On Solving Boolean Optimization with Satisfiability-Based Algorithms

This paper proposes new algorithms for the Binate Covering Problem (BCP), a well-known restriction of Boolean Optimization. Binate Covering finds application in many areas of Computer Science and Engineering. In Artificial Intelligence, BCP can be used for computing minimum-size prime implicants of Boolean functions, of interest in Automated Reasoning and Non-Monotonic Reasoning. Binate Covering is also an essential modeling tool in Electronic Design Automation (EDA). The objectives of the paper are to briefly review algorithmic solutions for BCP, and to describe how to apply search pruning techniques from the Boolean Satisfiability (SAT) domain to BCP. Furthermore, we generalize these pruning techniques, in particular the ability to backtrack non-chronologically, to exploit the actual formulation of the binate covering problem. Experimental results, obtained on representative instances indicate that the proposed techniques provide significant performance gains for different classes of instances.