Models and Algorithms for Computing Minimum-Size Prime Implicants

Minimum-size prime implicants of Boolean functions find application in many areas of Computer Science including, among others, Electronic Design Automation and Artificial Intelligence. The main purpose of this paper is to describe and evaluate two fundamentally different modeling and algorithmic solutions for the computation of minimum-size prime implicants. One is based on explicit search meth- ods, and uses Integer Linear Programming models and algorithms, whereas the other is based on implicit techniques, and so it uses Binary Decision Diagrams. For the explicit approach we propose new dedicated ILP algorithms, specifically target at solving these types of problems. As shown by the experi- mental results, other well-known ILP algorithms are in general impractical for computing minimum- size prime implicants. Moreover, we experimentally evaluate the two proposed algorithmic strategies.