Maximizing Submodular+Supermodular Functions Subject to a Fairness Constraint

Zhenning Zhang ¹Kaiqiao Meng ¹Donglei Du ²Yang Zhou³

扫码查看

作者信息

1. Beijing Institute for Scientific and Engineering Computing,Beijing University of Technology,Beijing 100124,China
2. Faculty of Management,University of New Brunswick,Fredericton E3B 5A3,Canada
3. School of Mathematics and Statistics,Shandong Normal University,Jinan 250014,China
折叠

Abstract

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.

Key words

submodular function/supermodular function/fairness constraint/greedy algorithm/threshold greedy algorithm/streaming algorithm

引用本文复制引用

基金项目

National Natural Science Foundation of China(12001025)

National Natural Science Foundation of China(12131003)

Spark Fund of Beijing University of Technology(XH-2021-06-03)

Natural Sciences and Engineering Research Council of Canada(283106)

Natural Science Foundation of China(11771386)

Natural Science Foundation of China(11728104)

National Natural Science Foundation of China(12001335)

出版年

2024

清华大学学报自然科学版(英文版)

清华大学

清华大学学报自然科学版(英文版)

CSTPCDEI

影响因子：0.474

ISSN：1007-0214

参考文献量17

段落导航