Research map / Learning theory and algorithms / Submodular and game algorithms
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Chen et al. (1/2-approx) unconstrained maximization instead of the 1/4-approx random-subset routine | Adaptive-Nonmonotone-Max · cardinality-constrained non-monotone submodular maximization | approximation factor: 0.026 -> 0.039 | Helped |
| Early termination of THRESHOLD-SAMPLING when candidate pool falls below c3k, followed by unconstrained maximization on remaining candidates | Adaptive-Nonmonotone-Max · theoretical analysis | adaptivity | 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)
- Consistent Submodular Maximization (ICML 2024 · similarity 0.92)
- Practical $0.385$-Approximation for Submodular Maximization Subject to a Cardinality Constraint (NeurIPS 2024 · similarity 0.92)
- A Unified Approach to Submodular Maximization Under Noise (NeurIPS 2025 · similarity 0.91)
- Online Two-Stage Submodular Maximization (NeurIPS 2025 · similarity 0.91)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.