首页|Maximizing Submodular+Supermodular Functions Subject to a Fairness Constraint

Maximizing Submodular+Supermodular Functions Subject to a Fairness Constraint

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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.

submodular functionsupermodular functionfairness constraintgreedy algorithmthreshold greedy algorithmstreaming algorithm

Zhenning Zhang、Kaiqiao Meng、Donglei Du、Yang Zhou

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Beijing Institute for Scientific and Engineering Computing,Beijing University of Technology,Beijing 100124,China

Faculty of Management,University of New Brunswick,Fredericton E3B 5A3,Canada

School of Mathematics and Statistics,Shandong Normal University,Jinan 250014,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaSpark Fund of Beijing University of TechnologyNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of ChinaNatural Science Foundation of ChinaNational Natural Science Foundation of China

1200102512131003XH-2021-06-03283106117713861172810412001335

2024

10.26599/TST.2022.9010013

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
年,卷(期)：2024.29(1)
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