Research map / Learning theory and algorithms / Combinatorial and search optimization
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LeapsAndBounds instead of Structured Procrastination | MiniSAT configuration search · CNFuzzDD SAT benchmark, no resume | total runtime (CPU days): 933.50 vs 1850.46 CPU days | Helped |
| Use LeapsAndBounds instead of Structured Procrastination | MiniSAT configuration search · CNFuzzDD SAT benchmark, with resume | total runtime (days): 368.50 vs 1169.36 days | Helped |
| Add empirical Bernstein stopping to RuntimeEst (ablation) | LeapsAndBounds · Low-variance runtime distributions | samples evaluated per phase: ε^{-1} rather than ε^{-2} | Helped |
| Use a smaller phase multiplier for θ (ablation) | LeapsAndBounds · Execution environment that allows resuming | total runtime | Helped |
| Use a smaller phase multiplier for θ (ablation) | LeapsAndBounds · Execution environment without resume | total runtime | Hurt |
Closest papers by meaning
- CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration (ICML 2019 · similarity 0.92)
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