Research map / Learning theory and algorithms / Submodular and game algorithms
Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Adding the binary-search variant of THRESHSEQ (cutting queries to ~O~(n) at cost of extra adaptivity) | PARKNAPSACK · Theory, general non-monotone knapsack | query complexity vs adaptivity | Mixed |
| Removing the value condition for monotone objectives | THRESHSEQ (monotone case) · Theory, monotone knapsack | approximation factor: 3+ε vs 9.465+ε | Helped |
| Parallel guessing of thresholds and multiple THRESHSEQ runs (instead of sequential threshold use) | PARKNAPSACK · Theory, cardinality constraint vs ADAPTIVE SEQUENCING | adaptivity: O(log n) vs O(log n log k) | Helped |
| Using ENV (Ene et al. 2019) as the comparison point on Max Weighted Cut | ENV algorithm · Max Weighted Cut small instances | approximation ratio: 3.7 at eps=0.1 vs 29 at eps=1/3 | Hurt |
| Using ParKnapsack (O~(n) query variant) rather than ENV on weighted cut | ParKnapsack vs ENV · Max Weighted Cut, adaptivity and objective | adaptive steps and weighted cut value | Helped |
| Using ParKnapsack instead of sequential Greedy/FANTOM/SampleGreedy | ParKnapsack · Movie Recommendation, Revenue Maximization, Graph Cut | objective value and adaptive steps | No clear effect |
Closest papers by meaning
- Practical Parallel Algorithms for Submodular Maximization Subject to a Knapsack Constraint with Nearly Optimal Adaptivity (AAAI 2023 · similarity 0.95)
- Breaking Barriers: Combinatorial Algorithms for Non-Monotone Submodular Maximization with Sublinear Adaptivity and $1/e$ Approximation (ICML 2025 · similarity 0.93)
- An Asymptotically Optimal Approximation Algorithm for Multiobjective Submodular Maximization at Scale (ICML 2025 · similarity 0.91)
- Discretely beyond $1/e$: Guided Combinatorial Algortihms for Submodular Maximization (NeurIPS 2024 · similarity 0.91)
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- Practical $0.385$-Approximation for Submodular Maximization Subject to a Cardinality Constraint (NeurIPS 2024 · similarity 0.90)
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