Research map / Learning theory and algorithms / Matrix and tensor methods
A Recursive Algorithm for Projected Model Counting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use projMC (recursive disjunctive decomposition) instead of D4P (dSharpP with D4) | projected model counting · 260 instances, 600s | instances solved: 140 -> 192 | Helped |
| use projMC instead of dSharpP | projected model counting · 260 instances, 600s | instances solved: 115 -> 192 | Helped |
| use projMC instead of #clasp | projected model counting · 260 instances, 600s | instances solved: 94 -> 192 | Helped |
| use projMC instead of d2c | projected model counting · 260 instances, 600s | instances solved: 71 -> 192 | Helped |
| use projMC instead of D4P on random Boolean circuit instances | projected model counting · circuit instances | solving time | Mixed |
| use D4 instead of dSharp as underlying counter in dSharpP-style algorithm (D4P vs dSharpP) | dSharpP · 260 instances, 600s | instances solved: 115 -> 140 | Helped |
| search for model maximizing clauses satisfied by existentially quantified literals to get fewer disjuncts (ablation) | projMC · 260 instances | running time | No clear effect |
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