Research map / Learning theory and algorithms / Submodular and game algorithms
Practical Parallel Algorithms for Submodular Maximization Subject to a Knapsack Constraint with Nearly Optimal Adaptivity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ParSKP1 instead of SampleGreedy | Non-monotone SKP · Revenue Maximization, Movie Recommendation, and Image Summarization | utility: average performance gains of 5% | Helped |
| Use ParSKP1 instead of SmkRanAcc | Non-monotone SKP · Revenue Maximization, Movie Recommendation, and Image Summarization | utility: average performance gains of 3% | Helped |
| Use ParSKP2 instead of SampleGreedy and SmkRanAcc | Non-monotone SKP · Revenue Maximization, Movie Recommendation, and Image Summarization | utility: average performance loss of 4% | Hurt |
| Use ParSKP1 or ParSKP2 instead of ParKnapsack | Non-monotone SKP · Revenue Maximization, Movie Recommendation, and Image Summarization | utility: up to 35.74% times | Helped |
| Use ParSKP1 or ParSKP2 instead of SmkRanAcc | Non-monotone SKP · Revenue Maximization, Movie Recommendation, and Image Summarization | adaptivity: 3–92 times fewer adaptive rounds | Helped |
| Use ParSKP1 or ParSKP2 instead of SampleGreedy | Non-monotone SKP · Revenue Maximization, Movie Recommendation, and Image Summarization | adaptivity: 2–54 times fewer adaptive rounds | Helped |
| Replace ParSKP1 with ParSKP2 | Non-monotone SKP · SKP approximation guarantee | approximation factor: 8 + ϵ -> 5 + 2√2 + ϵ | Helped |
| Apply ParSKP2 to the cardinality constraint case | ParSKP2 · Non-monotone SMC | approximation factor: 5 + 2√2 + ϵ -> 4 + ϵ | Helped |
Closest papers by meaning
- Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity (ICML 2021 · similarity 0.95)
- Breaking Barriers: Combinatorial Algorithms for Non-Monotone Submodular Maximization with Sublinear Adaptivity and $1/e$ Approximation (ICML 2025 · similarity 0.92)
- Consistent Submodular Maximization (ICML 2024 · similarity 0.91)
- Practical $0.385$-Approximation for Submodular Maximization Subject to a Cardinality Constraint (NeurIPS 2024 · similarity 0.91)
- An Asymptotically Optimal Approximation Algorithm for Multiobjective Submodular Maximization at Scale (ICML 2025 · similarity 0.91)
- Online and Streaming Algorithms for Constrained k-Submodular Maximization (AAAI 2025 · similarity 0.90)
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