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Research map / Learning theory and algorithms / Combinatorial and search optimization

A Sharp Leap from Quantified Boolean Formula to Stochastic Boolean Satisfiability Solving

AAAI 2021 · Learning theory and algorithms › Combinatorial and search optimization · approach: SAT and model counting solvers · 10 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use clause selection with integrated model counting to solve general multi-level SSATClauSSat · 318 SSAT formulasformulas solved exactly: 188 vs. 169Helped
Use clause selection with integrated model counting to solve two-level SSATClauSSat · 215 two-level SSAT instancesformulas solved exactly: 127 vs. 119Helped
Use clause selection and model counting rather than the compared solversClauSSat · All evaluated formulasmaximum memory usage (GB): 2.4 vs. 32.1Helped
Use ClauSSat instead of erSSATToiletA 10 05.3 · ToiletA 10 05.3satisfying probability lower bound: 1.56e-2 -> 3.13e-2Helped
Use ClauSSat instead of erSSATMPEC c5315-er · MPEC c5315-ersatisfying probability lower bound: 4.14e-1 -> 3.30e-1Hurt
Add cube distribution (ablation)ClauSSat-{m} · All evaluated formulasformulas solved exactly: +11 formulasHelped
Add cube distribution (ablation)ClauSSat-{m} · All evaluated formulasformulas with tighter bounds: +23 formulasHelped
Add non-chronological backtracking (ablation)ClauSSat-{mc} · All evaluated formulasformulas solved exactly: +18 formulasHelped
Add non-chronological backtracking (ablation)ClauSSat-{mc} · All evaluated formulasformulas with tighter bounds: +34 formulasHelped

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