Research map / Learning theory and algorithms / Combinatorial and search optimization
On Continuous Local BDD-Based Search for Hybrid SAT Solving
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use BDD-based belief-propagation gradients instead of FourierSAT gradient computation | GradSAT · 3-CNF, 10-CNF, 2-CNF+1 CARD, and 3-CNF+XORs | Gradient cost: 15.58x; 6.26; 4.65; 1.24 | Helped |
| Use a 16-core portfolio of continuous optimizers (ablation) | GradSAT on MaxSAT instances · MaxSAT instances | Avg. Score: 0.937 -> 0.970 | Helped |
| Use GradSAT in the solver portfolio | Virtual best solver · MaxSAT instances | Avg. Score: 0.990 -> 1 | Helped |
| Apply GradSAT to random CARD and PB formulas | GradSAT-16 cores · Random CARDs and PBs | instances solved: 732 instances | Helped |
| Apply GradSAT to XOR-rich formulas | GradSAT-16 cores · Random XORs-1CARD and CNF-XORs | instances solved: 155 vs 191 | Hurt |
| Apply GradSAT to large industrial partial MaxSAT instances | GradSAT · Industrial MaxSAT | instances answered: about 2/3 of all instances | Hurt |
| Share equivalent BDD nodes across constraints | MRBDD · MaxSAT-Random, MaxSAT-Crafted, and MaxSAT-Industrial | Avg. reduction ratio: 3.22; 2.51; 2.29 | Helped |
| Share equivalent BDD nodes across constraints | MRBDD · Hybrid SAT-CARDs and Hybrid SAT-PBs | Avg. reduction ratio: 1.02; 1.00 | No clear effect |
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