Research map / Learning theory and algorithms / Combinatorial and search optimization
BIRD: Engineering an Efficient CNF-XOR SAT Solver and Its Applications to Approximate Model Counting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| BIRD CNF-XOR handling (blast, in-process, recover XORs, Gauss-Jordan) in the SAT solver | ApproxMC2 · 1896 counting benchmarks | benchmarks solved: 492 -> 1140 | Helped |
| BIRD CNF-XOR handling in the SAT solver | ApproxMC2 · benchmarks solved by both | runtime speedup: mean speedup 284.40 | Helped |
| BIRD CNF-XOR handling in the SAT solver | ApproxMC2 · benchmarks with exact counts | observed tolerance: 0.37 -> 0.038 | Helped |
| hashing-based approximate counter ApproxMC3 vs exact counter DSharp | model counting · 1896 counting benchmarks | benchmarks solved: 1001 vs 1140 | Helped |
Closest papers by meaning
- Towards Real-Time Approximate Counting (AAAI 2025 · similarity 0.90)
- Approximate Probabilistic Inference via Word-Level Counting (AAAI 2016 · similarity 0.89)
- Auditable Algorithms for Approximate Model Counting (AAAI 2024 · similarity 0.89)
- Towards Projected and Incremental Pseudo-Boolean Model Counting (AAAI 2025 · similarity 0.86)
- Engineering an Exact Pseudo-Boolean Model Counter (AAAI 2024 · similarity 0.86)
- Model Counting and Sampling via Semiring Extensions (AAAI 2024 · similarity 0.86)
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