On using satisfiability-based pruning techniques in covering algorithms
DATE '00 Proceedings of the conference on Design, automation and test in Europe
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ACM Transactions on Design Automation of Electronic Systems (TODAES)
Satisfiability-Based Algorithms for Boolean Optimization
Annals of Mathematics and Artificial Intelligence
Prime Implicates and Reduced Implicate Tries
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
TABLEAUX '09 Proceedings of the 18th International Conference on Automated Reasoning with Analytic Tableaux and Related Methods
Identifying Prime Implicate Branches in Reduced Implicate Tries
Fundamenta Informaticae - Methodologies for Intelligent Systems
Solving satisfiability problems with preferences
Constraints
Prime forms and minimal change in propositional belief bases
Annals of Mathematics and Artificial Intelligence
Approximate quantifier elimination for propositional boolean formulae
NFM'11 Proceedings of the Third international conference on NASA Formal methods
Tri-based set operations and selective computation of prime implicates
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Existential quantification as incremental SAT
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Minimum satisfying assignments for SMT
CAV'12 Proceedings of the 24th international conference on Computer Aided Verification
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The computation of prime implicants has several and significant applications in different areas, including Automated Reasoning, Non-Monotonic Reasoning, Electronic Design Automation, among others. In this paper we describe a new model and algorithm for computing minimum-size prime implicants of propositional formulas. The proposed approach is based on creating an integer linear program (ILP) formulation for computing the minimum-size prime implicant, which simplifies existing formulations. In addition, we introduce two new algorithms for solving ILPs, both of which are built on top of an algorithm for propositional satisfiability (SAT). Given the organization of the proposed SAT algorithm, the resulting ILP procedures implement powerful search pruning techniques, including a non-chronological backtracking search strategy, clause recording procedures and identification of necessary assignments. Experimental results, obtained on several benchmark examples, indicate that the proposed model and algorithms are significantly more efficient than other existing solutions.