Research map / Learning theory and algorithms / Prediction and decision losses
ADDMC: Weighted Model Counting with Algebraic Decision Diagrams
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use BM-Tree clustering with LexP cluster order and MCS diagram order rather than the median heuristic configuration (ablation) | ADDMC · 1914 CNF weighted model-counting benchmarks, 10-second timeout | benchmarks solved: 504 -> 1202 | Helped |
| Replace random cluster and diagram variable orders with the Best1 heuristic configuration (ablation) | ADDMC · 1914 CNF weighted model-counting benchmarks, 10-second timeout | benchmarks solved: 53 -> 1202 | Helped |
| Use BM-Tree rather than BE-Tree with the listed top variable orders (ablation) | ADDMC · 1914 CNF weighted model-counting benchmarks, 10-second timeout | benchmarks solved: 1200 -> 1202 | No clear effect |
| Add ADDMC to the virtual best solver portfolio | VBS0 without ADDMC · 1914 CNF weighted model-counting benchmarks, 1000-second timeout | benchmarks solved: 1647 -> 1771 | Helped |
| Use ADDMC alongside the other counters in the portfolio | Virtual best solver · 1914 CNF weighted model-counting benchmarks, 1000-second timeout | benchmarks fastest: 763 benchmarks | Helped |
Closest papers by meaning
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- The Gradient of Algebraic Model Counting (AAAI 2025 · similarity 0.86)
- An Approximate Skolem Function Counter (AAAI 2024 · similarity 0.86)
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