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This is a Quantitative Study study.

September 20, 2025

Fair Submodular Maximization over a Knapsack Constraint

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Authors

LLLijun LiHunan Institute of Science and Technology CXChenyang XuQingdao Agricultural University LYLiuyi YangZhejiang University of Finance and Economics

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Implication

This research reveals a constant approximation algorithm for submodular maximization with knapsack constraints, suggesting efficiency improvements in fairness applications.

Key Points

A polynomial-time algorithm achieves constant approximation under the assumption of a constant number of colors.
Under relaxed constraints, a tight approximation ratio of (1-1/e-epsilon) can be obtained for various epsilon values.
Imposing both knapsack and fairness constraints poses a significant challenge with no trivial solutions known.
The findings have implications for diverse fields including economics, machine learning, and data mining.

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/68d46aa631b076d99fa6726d https://doi.org/10.24963/ijcai.2025/438

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Implication

This research reveals a constant approximation algorithm for submodular maximization with knapsack constraints, suggesting efficiency improvements in fairness applications.

Fair Submodular Maximization over a Knapsack Constraint

Discussion

Member takes

Implication

Key Points

Cite This Study

Fair Submodular Maximization over a Knapsack Constraint

Discussion

Member takes

Implication

Key Points

Cite This Study

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