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An approximation algorithm for lower-bounded k-median with constant factor

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  • Special Focus on Theory and Applications of Models of Computation
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Abstract

The lower-bounded k-median problem plays a key role in many applications related to privacy protection, which requires that the amount of assigned client to each facility should not be less than the requirement. Unfortunately, the lower-bounded clustering problem remains elusive under the widely studied k-median objective. Within this paper, we convert this problem to the capacitated facility location problem and successfully give a (516 + ϵ-approximation for this problem.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61872450, 62172446, 61802441).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Xiaoliang Wu, Feng Shi, Yutian Guo, Junyu Huang & Jianxin Wang

  2. Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China

    Xiaoliang Wu, Feng Shi, Yutian Guo, Junyu Huang & Jianxin Wang

  3. School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha, 410083, China

    Zhen Zhang

Authors
  1. Xiaoliang Wu
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  2. Feng Shi
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  3. Yutian Guo
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  4. Zhen Zhang
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  5. Junyu Huang
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  6. Jianxin Wang
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Correspondence to Feng Shi.

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Wu, X., Shi, F., Guo, Y. et al. An approximation algorithm for lower-bounded k-median with constant factor. Sci. China Inf. Sci. 65, 140601 (2022). https://doi.org/10.1007/s11432-021-3411-7

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  • DOI: https://doi.org/10.1007/s11432-021-3411-7

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