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Maximizing Submodular plus Supermodular Functions Subject to a Fairness Constraint
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DOI：10.26599/TST.2022.9010013.png)
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Abstract
En 中文
We investigate the problem of maximizing the sum of submodular and supermodular functions under a fairness constraint. This sum function is non-submodular in general. For an offline model, we introduce two approximation algorithms: A greedy algorithm and a threshold greedy algorithm. For a streaming model, we propose a one-pass streaming algorithm. We also analyze the approximation ratios of these algorithms, which all depend on the total curvature of the supermodular function. The total curvature is computable in polynomial time and widely utilized in the literature.
Keywords:
Greedy algorithms
Computational modeling
Big Data
Approximation algorithms
Complexity theory
submodular function
supermodular function
fairness constraint
greedy algorithm
threshold greedy algorithm
streaming algorithm
Journal
T
IF:
3.5
Papers:
987
Citations:
2.5K
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