Maximizing Monotone Submodular Functions over the Integer Lattice
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Abstract (arXiv)
The problem of maximizing non-negative monotone submodular functions under a certain constraint has been intensively studied in the last decade. In this paper, we address the problem for functions defined over the integer lattice. Suppose that a non-negative monotone submodular function f:mathbbZ_+n → ℝ_+ is given via an evaluation oracle. Assume further that f satisfies the diminishing return property, which is not an immediate consequence of submodularity when the domain is the integer lattice. Given this, we design polynomial-time (1-1/e-ε)-approximation algorithms for a cardinality constraint, a polymatroid constraint, and a knapsack constraint. For a cardinality constraint, we also provide a (1-1/e-ε)-approximation algorithm with slightly worse time complexity that does not rely on the diminishing return property.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Mar 2015.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
5 papers.
- 2021Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimizationcited by 40
- 2026Q-Strata: Hierarchical Bit Allocation for Mixed-Precision Quantization of Mixture-of-Experts LLMscited by 2
- 2021Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequencescited by 1
- 2021Randomized Algorithms for Monotone Submodular Function Maximization on the Integer Latticecited by 1
- 2025Fast Approximation Algorithm for Non-Monotone DR-submodular Maximization under Size Constraintcited by 0
- 2026Q-Strata: Hierarchical Bit Allocation for Mixed-Precision Quantization of Mixture-of-Experts LLMs31 Aug 2026
- 2025Fast Approximation Algorithm for Non-Monotone DR-submodular Maximization under Size Constraint4 Nov 2025
- 2021Randomized Algorithms for Monotone Submodular Function Maximization on the Integer Lattice19 Nov 2021
- 2021Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences20 Apr 2021
- 2021Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization18 Feb 2021