Research map / Learning theory and algorithms / Submodular and game algorithms
Submodular Maximization under the Intersection of Matroid and Knapsack Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SPROUT++ instead of the compared algorithms | Movie recommendation · Movie recommendation | objective value | Helped |
| Use SPROUT++ instead of the compared algorithms | Weighted max-cut · Weighted max-cut | objective value | Helped |
| Increase the random enumeration count t_c (ablation) | SPROUT++ · Weighted max-cut | objective value | Helped |
| Increase the random enumeration count t_c (ablation) | SPROUT++ · Weighted max-cut | standard deviation | Helped |
| Increase the smooth parameter μ (ablation) | SPROUT++ · Movie recommendation with three knapsack constraints | objective value | Helped |
| Use SPROUT++ rather than SPROUT (ablation) | Submodular maximization under matroid and knapsack constraints · Experiments in Figure [ref](a) | oracle calls | Helped |
| Use SPROUT++ rather than SPROUT (ablation) | Submodular maximization under matroid and knapsack constraints · Experiments in Figure [ref](a) | objective value | No clear effect |
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