Research map / Learning theory and algorithms / Submodular and game algorithms
Interlaced Greedy Algorithm for Maximization of Submodular Functions in Nearly Linear Time
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Interlace two greedy procedures instead of running the greedy procedures successively | Deterministic MCC maximization · MCC | approximation ratio: 1/6 -> 1/4 | Helped |
| Use thresholded interlaced greedy procedures | FastInterlaceGreedy · MCC | query complexity: O(kn) -> O\left( \frac{n}{\epsilon} \log \frac{k}{\epsilon} \right) | Helped |
| Add the stealing procedure to recover useful elements excluded during interlaced selection (ablation) | FastInterlaceGreedy · ER maximum cut | solution value increase: at most 1.5% | Helped |
| Add the stealing procedure to recover useful elements excluded during interlaced selection (ablation) | FastInterlaceGreedy · BA maximum cut | solution value increase: up to 45% | Helped |
| Remove the stealing procedure (ablation) | FastInterlaceGreedy · BA maximum cut | solution value: up to 45% | Hurt |
| Use FastInterlaceGreedy rather than the deterministic iterated-greedy baseline | Maximum cut and social network monitoring · Evaluated application instances | objective value | No clear effect |
| Use FastInterlaceGreedy rather than the deterministic iterated-greedy baseline | Maximum cut and social network monitoring · Evaluated application instances | queries to the objective | Helped |
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