Verification Using Uninterpreted Functions and Finite Instantiations
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ICALP '98 Proceedings of the 25th International Colloquium on Automata, Languages and Programming
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CAV '98 Proceedings of the 10th International Conference on Computer Aided Verification
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FMCAD '00 Proceedings of the Third International Conference on Formal Methods in Computer-Aided Design
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CAV '01 Proceedings of the 13th International Conference on Computer Aided Verification
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CAV '01 Proceedings of the 13th International Conference on Computer Aided Verification
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CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
Exploiting Positive Equality in a Logic of Equality with Uninterpreted Functions
CAV '99 Proceedings of the 11th International Conference on Computer Aided Verification
Finite Instantiations in Equivalence Logic with Uninterpreted Functions
CAV '01 Proceedings of the 13th International Conference on Computer Aided Verification
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Building small equality graphs for deciding equality logic with uninterpreted functions
Information and Computation
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Journal of Computer Science and Technology - Special issue on natural language processing
Yet another decision procedure for equality logic
CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
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CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
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RTA'05 Proceedings of the 16th international conference on Term Rewriting and Applications
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SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
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We introduce an efficient decision procedure for the theory of equality based on finite instantiations. When using the finite instantiations method, it is a common practice to take a range of [1..n] (where n is the number of input non-Boolean variables) as the range for all non-Boolean variables, resulting in a state-space of nn. Although various attempts to minimize this range were made, typically they either required various restrictions on the investigated formulas or were not very effective. In many cases, the nn state-space cannot be handled by BDD-based tools within a reasonable amount of time. In this paper we show that significantly smaller domains can be algorithmically found, by analyzing the structure of the formula. We also show an upper bound for the state-space based on this analysis. This method enabled us to verify formulas containing hundreds of integer and floating point variables.