Research map / Learning theory and algorithms / Submodular and game algorithms
Greedy Maximization of Functions with Bounded Curvature under Partition Matroid Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use deterministic Greedy for submodular maximization with bounded curvature, without requiring monotonicity | Greedy · Partition matroid constraints, submodular functions | approximation ratio: \frac{1}{\alpha} ( 1 - e^{-\alpha \overline{d}/d}) | Helped |
| Use deterministic Greedy for monotone subadditive maximization with bounded curvature | Greedy · Partition matroid constraints, monotone subadditive functions | approximation ratio: \frac{1}{\alpha} ( 1 - e^{(\alpha^2 -\alpha )\overline{d}/d}) | Helped |
| Apply deterministic Greedy to real-world directed-cut graphs | Greedy · 20 Network Repository instances, unconstrained maximum directed cut | solution quality relative to optimum | Helped |
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