Research map / Learning theory and algorithms / Combinatorial and search optimization
A Sharp Leap from Quantified Boolean Formula to Stochastic Boolean Satisfiability Solving
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use clause selection with integrated model counting to solve general multi-level SSAT | ClauSSat · 318 SSAT formulas | formulas solved exactly: 188 vs. 169 | Helped |
| Use clause selection with integrated model counting to solve two-level SSAT | ClauSSat · 215 two-level SSAT instances | formulas solved exactly: 127 vs. 119 | Helped |
| Use clause selection and model counting rather than the compared solvers | ClauSSat · All evaluated formulas | maximum memory usage (GB): 2.4 vs. 32.1 | Helped |
| Use ClauSSat instead of erSSAT | ToiletA 10 05.3 · ToiletA 10 05.3 | satisfying probability lower bound: 1.56e-2 -> 3.13e-2 | Helped |
| Use ClauSSat instead of erSSAT | MPEC c5315-er · MPEC c5315-er | satisfying probability lower bound: 4.14e-1 -> 3.30e-1 | Hurt |
| Add cube distribution (ablation) | ClauSSat-{m} · All evaluated formulas | formulas solved exactly: +11 formulas | Helped |
| Add cube distribution (ablation) | ClauSSat-{m} · All evaluated formulas | formulas with tighter bounds: +23 formulas | Helped |
| Add non-chronological backtracking (ablation) | ClauSSat-{mc} · All evaluated formulas | formulas solved exactly: +18 formulas | Helped |
| Add non-chronological backtracking (ablation) | ClauSSat-{mc} · All evaluated formulas | formulas with tighter bounds: +34 formulas | Helped |
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