Constrained Submodular Maximization: Beyond 1/e
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Abstract (arXiv)
In this work, we present a new algorithm for maximizing a non-monotone submodular function subject to a general constraint. Our algorithm finds an approximate fractional solution for maximizing the multilinear extension of the function over a down-closed polytope. The approximation guarantee is 0.372 and it is the first improvement over the 1/e approximation achieved by the unified Continuous Greedy algorithm [Feldman et al., FOCS 2011].
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Aug 2016.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 3; 2025: 0; 2026: 1.
7 papers.
- 2022Using Partial Monotonicity in Submodular Maximizationcited by 9
- 2023Submodular Minimax Optimization: Finding Effective Setscited by 7
- 2024Practical 0.385-Approximation for Submodular Maximization Subject to a Cardinality Constraintcited by 3
- 2021The Power of Subsampling in Submodular Maximizationcited by 2
- 2024Bridging the Gap Between General and Down-Closed Convex Sets in Submodular Maximizationcited by 1
- 2024Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Banditcited by 0
- 2026Strong and Compact Policies for Submodular Markov Decision Processes via LP-Based Submodular Orienteeringcited by 0
- 2026Strong and Compact Policies for Submodular Markov Decision Processes via LP-Based Submodular Orienteering14 Sep 2026
- 2024Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit11 Oct 2024
- 2024Practical 0.385-Approximation for Submodular Maximization Subject to a Cardinality Constraint22 May 2024
- 2024Bridging the Gap Between General and Down-Closed Convex Sets in Submodular Maximization17 Jan 2024
- 2023Submodular Minimax Optimization: Finding Effective Sets26 May 2023
- 2022Using Partial Monotonicity in Submodular Maximization7 Feb 2022
- 2021The Power of Subsampling in Submodular Maximization6 Apr 2021