Research map / Learning theory and algorithms / Submodular and game algorithms
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GAME instead of BMBK | continuous bi-greedy optimization · NQP strong-DR | average objective value: 1225.738044 -> 1225.840375 | No clear effect |
| Use BINARY instead of BMBK | strong-DR continuous bi-greedy optimization · NQP strong-DR | average objective value: 1225.738044 -> 1225.816408 | No clear effect |
| Use GAME instead of BMBK | continuous bi-greedy optimization · NQP weak-DR | average objective value: 1203.424957 -> 1203.288522 | No clear effect |
| Use BINARY instead of BMBK | strong-DR continuous bi-greedy optimization · NQP weak-DR | average objective value: 1203.424957 -> 1202.737999 | No clear effect |
| Use GAME instead of BMBK | continuous bi-greedy optimization · Softmax extension (DPP, strong-DR) | average objective value: 24.055435 -> 24.056934 | No clear effect |
| Use BINARY instead of BMBK | strong-DR continuous bi-greedy optimization · Softmax extension (DPP, strong-DR) | average objective value: 24.055435 -> 23.945428 | No clear effect |
Closest papers by meaning
- Continuous Submodular Maximization: Beyond DR-Submodularity (NeurIPS 2020 · similarity 0.92)
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- Consistent Submodular Maximization (ICML 2024 · similarity 0.92)
- Online Two-Stage Submodular Maximization (NeurIPS 2025 · similarity 0.91)
- A Unified Approach to Submodular Maximization Under Noise (NeurIPS 2025 · similarity 0.91)
- Breaking Barriers: Combinatorial Algorithms for Non-Monotone Submodular Maximization with Sublinear Adaptivity and $1/e$ Approximation (ICML 2025 · similarity 0.91)
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