Online Weakly DR-Submodular Optimization with Stochastic Long-Term Constraints

Feng, Junkai; Yang, Ruiqi; Zhang, Yapu; Zhang, Zhenning

doi:10.1007/978-3-031-20350-3_4

Lecture Notes in Computer Science

2022

DOI: 10.1007/978-3-031-20350-3_4

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Online Weakly DR-Submodular Optimization with Stochastic Long-Term Constraints

Junkai Feng

Ruiqi Yang

Yapu Zhang

et al.

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2024

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“…For some certain settings of and , different online algorithms have been proposed [16][17][18][19] .…”

mentioning

confidence: 99%

Online Weakly DR-Submodular Optimization Under Stochastic Cumulative Constraints

Feng,

Yang,

Zhang

et al. 2024

Tsinghua Sci. Technol.

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In this paper, we study a class of online continuous optimization problems. At each round, the utility function is the sum of a weakly diminishing-returns (DR) submodular function and a concave function, certain cost associated with the action will occur, and the problem has total limited budget. Combining the two methods, the penalty function and Frank-Wolfe strategies, we present an online method to solve the considered problem. Choosing appropriate stepsize and penalty parameters, the performance of the online algorithm is guaranteed, that is, it achieves sub-linear regret bound and certain mild constraint violation bound in expectation.

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“…For some certain settings of and , different online algorithms have been proposed [16][17][18][19] .…”

mentioning

confidence: 99%

Online Weakly DR-Submodular Optimization Under Stochastic Cumulative Constraints

Feng,

Yang,

Zhang

et al. 2024

Tsinghua Sci. Technol.

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show abstract

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Online Weakly DR-Submodular Optimization with Stochastic Long-Term Constraints

Cited by 1 publication

References 6 publications

Online Weakly DR-Submodular Optimization Under Stochastic Cumulative Constraints

Online Weakly DR-Submodular Optimization Under Stochastic Cumulative Constraints

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