Research map / Learning theory and algorithms / Combinatorial and search optimization
Enhancing Lazy Grounding with Lazy Normalization in Answer-Set Programming
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| lazy normalization of count aggregates in a lazy-grounding solver instead of ground-and-solve (Clingo) | Alpha · Simulation | CPU time (s): size 100: 2.6 vs 0.8; size 500: 9.1 vs 110.5 | Mixed |
| lazy normalization of sum aggregates vs Clingo | Alpha · Summation | CPU time (s): 96.8 -> 3.0 at size 300 | Helped |
| lazy normalization vs Clingo/Dlv2 on indegree counting | Alpha · Dynamic Indegree Counting | CPU time (s): 12k: 36.5 vs 271.8 (Clingo), 9.6 (Dlv2) | Mixed |
| lazy normalization vs ground-and-solve on aggregate with n-tuple domain | Alpha · Exponential Space Saving, size 22 | memory: 2.06 GB -> 82 MB | Helped |
| use sorting-network (odd-even) compact count normalization instead of counting grid (ablation) | Alpha lazy normalization · count aggregate benchmarks | performance | Helped |
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