Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Combinations of abstract domains for logic programming
POPL '94 Proceedings of the 21st ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Two classes of Boolean functions for dependency analysis
Science of Computer Programming
Confluence in Concurrent Constraint Programming
AMAST '95 Proceedings of the 4th International Conference on Algebraic Methodology and Software Technology
A Reactive Implementation of Pos Using ROBDDs
PLILP '96 Proceedings of the 8th International Symposium on Programming Languages: Implementations, Logics, and Programs
Immediate Fixpoints and Their Use in Groundness Analysis
Proceedings of the 16th Conference on Foundations of Software Technology and Theoretical Computer Science
Conceptual and Software Support for Abstract Domain Design: Generic Structural Domain and Open Product
Positive Boolean Functions as Multiheaded Clauses
Proceedings of the 17th International Conference on Logic Programming
Decomposing Non-redundant Sharing by Complementation
SAS '99 Proceedings of the 6th International Symposium on Static Analysis
Parameterizing a Groundness Analysis of Logic Programs
SAS '01 Proceedings of the 8th International Symposium on Static Analysis
Widening ROBDDs with prime implicants
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Boolean equi-propagation for concise and efficient SAT encodings of combinatorial problems
Journal of Artificial Intelligence Research
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The subject of groundness analysis for (constraint) logic programs has been widely studied, and interesting domains have been proposed. Pos has been recognized as the most suitable domain for capturing the kind of dependencies arising in groundness analysis, and Reduced Ordered Binary Decision Diagrams (ROBDDs) are generally accepted to be the most efficient representation for Pos. Unfortunately, the size of an ROBDDs is, in the worst case, exponential in the number of variables it depends upon. Earlier work [2] has shown that a hybrid representation that separates the definite information from the dependency information is considerably more efficient than keeping the two together. The aim of the present paper is to push this idea further, also separating out certain dependency information, in particular all pairs of variables that are always either both ground or neither ground. We find that this new hybrid representation is a significant improvement over previous work.