Research map / Learning theory and algorithms / Submodular and game algorithms
Nearly Linear-Time, Parallelizable Algorithms for Non-Monotone Submodular Maximization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Improve the analysis and use ATG instead of ADAPTIVENONMONOTONEMAX | Nearly linear-time adaptive algorithms for SMCC · SMCC | approximation ratio: 0.039 − ε -> 0.193 − ε | Helped |
| Use ATG rather than other evaluated parallelizable algorithms | Cardinality-constrained maximum cut on web-Google · web-Google maxcut | objective value relative to ITERATEDGREEDY: better than 0.99 | Helped |
| Use ATG rather than AST | Evaluated SMCC applications · SMCC applications | objective value: up to 18% | Helped |
| Use Ene and Nguyˆen (2020) rather than ATG | Cardinality-constrained maximum cut on web-Google · web-Google maxcut, larger k values | objective value relative to ITERATEDGREEDY: about 0.75 | Hurt |
| Use Ene and Nguyˆen (2020) rather than the other evaluated algorithms | Cardinality-constrained maximum cut on web-Google · web-Google maxcut, k ≤ 100 | objective value | Hurt |
| Approximate the multilinear-extension oracles through sampling rather than use direct oracle access | Ene and Nguyˆen (2020) · Revenue maximization | query efficiency | Hurt |
Closest papers by meaning
- Best of Both Worlds: Practical and Theoretically Optimal Submodular Maximization in Parallel (NeurIPS 2021 · similarity 0.96)
- Breaking Barriers: Combinatorial Algorithms for Non-Monotone Submodular Maximization with Sublinear Adaptivity and $1/e$ Approximation (ICML 2025 · similarity 0.95)
- Consistent Submodular Maximization (ICML 2024 · similarity 0.92)
- An Asymptotically Optimal Approximation Algorithm for Multiobjective Submodular Maximization at Scale (ICML 2025 · similarity 0.92)
- Discretely beyond $1/e$: Guided Combinatorial Algortihms for Submodular Maximization (NeurIPS 2024 · similarity 0.92)
- A Unified Approach to Submodular Maximization Under Noise (NeurIPS 2025 · similarity 0.91)
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