Multipass Streaming Algorithms for Regularized Submodular Maximization  

作　　者：Qinqin Gong Suixiang Gao Fengmin Wang Ruiqi Yang 

机构地区：[1]Beijing Institute for Scientific and Engineering Computing,Beijing University of Technology,Beijing 100124.China [2]School of Mathematical Sciences,University of Chinese Academy Sciences,Beijing 100049,China [3]Beijing Jinghang Research Institute of Computing and Communication,Beijing 100074,China

出　　处：《Tsinghua Science and Technology》2024年第1期76-85,共10页清华大学学报（自然科学版（英文版）

基　　金：This work was supported by the Beijing Natural Science Foundation Project(No.Z220004);the National Natural Science Foundation of China(Nos.11901544 and 12101587);the China Postdoctoral Science Foundation(No.2022M720329).

摘　　要：In this work,we study a k-Cardinality Constrained Regularized Submodular Maximization(k-CCRSM)problem,in which the objective utility is expressed as the difference between a non-negative submodular and a modular function.No multiplicative approximation algorithm exists for the regularized model,and most works have focused on designing weak approximation algorithms for this problem.In this study,we consider the k-CCRSM problem in a streaming fashion,wherein the elements are assumed to be visited individually and cannot be entirely stored in memory.We propose two multipass streaming algorithms with theoretical guarantees for the above problem,wherein submodular terms are monotonic and nonmonotonic.

关 键 词：submodular optimization regularized model streaming algorithms THRESHOLD 

分 类 号：O157.5[理学—数学]