Representing circuits more efficiently in symbolic model checking
DAC '91 Proceedings of the 28th ACM/IEEE Design Automation Conference
Finding good approximate vertex and edge partitions is NP-hard
Information Processing Letters
To split or to conjoin: the question in image computation
Proceedings of the 37th Annual Design Automation Conference
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Border-Block Triangular Form and Conjunction Schedule in Image Computation
FMCAD '00 Proceedings of the Third International Conference on Formal Methods in Computer-Aided Design
Efficient Model Checking by Automated Ordering of Transition Relation Partitions
CAV '94 Proceedings of the 6th International Conference on Computer Aided Verification
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
Optimizing model checking based on bdd characterization
Optimizing model checking based on bdd characterization
FMCAD '02 Proceedings of the 4th International Conference on Formal Methods in Computer-Aided Design
New metrics for static variable ordering in decision diagrams
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Efficient guided symbolic reachability using reachability expressions
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
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Model checking is the process of verifying whether a model of a concurrent system satisfies a specified temporal property. Symbolic algorithms based on Binary Decision Diagrams (BDDs) have significantly increased the size of the models that can be verified. The main problem in symbolic model checking is the image computation problem, i.e., efficiently computing the successors or predecessors of a set of states. This paper is an in-depth study of the image computation problem. We analyze and evaluate several newheuristics, metrics, and algorithms for this problem. The algorithms use combinatorial optimization techniques such as hill climbing, simulated annealing, and ordering by recursive partitioning to obtain better results than was previously the case. Theoretical analysis and systematic experimentation are used to evaluate the algorithms.