Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice
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Abstract (arXiv)
The optimization of submodular functions on the integer lattice has received much attention recently, but the objective functions of many applications are non-submodular. We provide two approximation algorithms for maximizing a non-submodular function on the integer lattice subject to a cardinality constraint; these are the first algorithms for this purpose that have polynomial query complexity. We propose a general framework for influence maximization on the integer lattice that generalizes prior works on this topic, and we demonstrate the efficiency of our algorithms in this context.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 May 2018.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 1; 2025: 1.
6 papers.
- 2021Training Data Subset Selection for Regression with Controlled Generalization Errorcited by 14
- 2022Using Partial Monotonicity in Submodular Maximizationcited by 9
- 2022Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedbackcited by 1
- 2022Online Subset Selection using α-Core with no Augmented Regretcited by 0
- 2024Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete Optimizationcited by 0
- 2025Fast Approximation Algorithm for Non-Monotone DR-submodular Maximization under Size Constraintcited by 0
- 2025Fast Approximation Algorithm for Non-Monotone DR-submodular Maximization under Size Constraint4 Nov 2025
- 2024Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete Optimization23 Oct 2024
- 2022Online Subset Selection using α-Core with no Augmented Regret28 Sep 2022
- 2022Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback15 May 2022
- 2022Using Partial Monotonicity in Submodular Maximization7 Feb 2022
- 2021Training Data Subset Selection for Regression with Controlled Generalization Error23 Jun 2021