Approximate SMT Counting Beyond Discrete Domains
Satisfiability Modulo Theory (SMT) solvers have advanced automated reasoning, solving complex formulas across discrete and continuous domains. Recent progress in propositional model counting motivates extending SMT capabilities toward model counting, especially for hybrid SMT formulas. Existing appr...
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| Published in: | 2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7 |
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| Format: | Conference Proceeding |
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IEEE
22.06.2025
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| Abstract | Satisfiability Modulo Theory (SMT) solvers have advanced automated reasoning, solving complex formulas across discrete and continuous domains. Recent progress in propositional model counting motivates extending SMT capabilities toward model counting, especially for hybrid SMT formulas. Existing approaches, like bit-blasting, are limited to discrete variables, highlighting the challenge of counting solutions projected onto the discrete domain in hybrid formulas. We introduce pact, an SMT model counter for hybrid formulas that uses hashing-based approximate model counting to estimate solutions with theoretical guarantees. pact makes a logarithmic number of SMT solver calls relative to the projection variables, leveraging optimized hash functions. pact achieves significant performance improvements over baselines on a large suite of benchmarks. In particular, out of \mathbf{1 4, 2 0 2} instances, pact successfully finished on 603 instances, while Baseline could only finish on 13 instances. |
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| AbstractList | Satisfiability Modulo Theory (SMT) solvers have advanced automated reasoning, solving complex formulas across discrete and continuous domains. Recent progress in propositional model counting motivates extending SMT capabilities toward model counting, especially for hybrid SMT formulas. Existing approaches, like bit-blasting, are limited to discrete variables, highlighting the challenge of counting solutions projected onto the discrete domain in hybrid formulas. We introduce pact, an SMT model counter for hybrid formulas that uses hashing-based approximate model counting to estimate solutions with theoretical guarantees. pact makes a logarithmic number of SMT solver calls relative to the projection variables, leveraging optimized hash functions. pact achieves significant performance improvements over baselines on a large suite of benchmarks. In particular, out of \mathbf{1 4, 2 0 2} instances, pact successfully finished on 603 instances, while Baseline could only finish on 13 instances. |
| Author | Shaw, Arijit Meel, Kuldeep S. |
| Author_xml | – sequence: 1 givenname: Arijit surname: Shaw fullname: Shaw, Arijit organization: Chennai Mathematical Institute,India – sequence: 2 givenname: Kuldeep S. surname: Meel fullname: Meel, Kuldeep S. organization: Georgia Institute of Technology,USA |
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| Snippet | Satisfiability Modulo Theory (SMT) solvers have advanced automated reasoning, solving complex formulas across discrete and continuous domains. Recent progress... |
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| SubjectTerms | Benchmark testing Cognition Design automation Hash functions |
| Title | Approximate SMT Counting Beyond Discrete Domains |
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