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Open Access

Bicriteria Algorithms for Approximately Submodular Cover Under Streaming Model

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
School of Mathematics and Statistics, Ningbo University, Ningbo 315211, China
Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing 100124, China
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Abstract

In this paper, we mainly investigate the optimization model that minimizes the cost function such that the cover function exceeds a required threshold in the set cover problem, where the cost function is additive linear, and the cover function is non-monotone approximately submodular. We study the problem under streaming model and propose three bicriteria approximation algorithms. Firstly, we provide an intuitive streaming algorithm under the assumption of known optimal objective value. The intuitive streaming algorithm returns a solution such that its cover function value is no less than α(1-ϵ) times threshold, and the cost function is no more than (2+ϵ)2/(ϵ2ω2)κ, where κ is a value that we suppose for the optimal solution and α is the approximation ratio of an algorithm for unconstrained maximization problem that we can call directly. Next we present a bicriteria streaming algorithm scanning the ground set multi-pass to weak the assumption that we guess the optimal objective value in advance, and maintain the same bicriteria approximation ratio. Finally we modify the multi-pass streaming algorithm to a single-pass one without compromising the performance ratio. Additionally, we also propose some numerical experiments to test our algorithm’s performance comparing with some existing methods.

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Tsinghua Science and Technology
Pages 1030-1040
Cite this article:
Wang Y, Yang X, Zhang H, et al. Bicriteria Algorithms for Approximately Submodular Cover Under Streaming Model. Tsinghua Science and Technology, 2023, 28(6): 1030-1040. https://doi.org/10.26599/TST.2022.9010061

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Received: 02 August 2022
Revised: 24 October 2022
Accepted: 28 November 2022
Published: 28 July 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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