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Approximation Algorithms for the Capacitated Min–Max Correlation Clustering Problem

Author

Listed:
  • Sai Ji

    (Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P. R. China)

  • Jun Li

    (College of Statistics and Data Science, Beijing University of Technology, Beijing 100124, P. R. China)

  • Zijun Wu

    (Institute for Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230000, P. R. China)

  • Yicheng Xu

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P. R. China5Guangxi Key Laboratory of Cryptography and Information Security, Guilin 541004, P. R. China)

Abstract

In this paper, we propose a so-called capacitated min–max correlation clustering model, a natural variant of the min–max correlation clustering problem. As our main contribution, we present an integer programming and its integrality gap analysis for the proposed model. Furthermore, we provide two approximation algorithms for the model, one of which is a bi-criteria approximation algorithm and the other is based on LP-rounding technique.

Suggested Citation

  • Sai Ji & Jun Li & Zijun Wu & Yicheng Xu, 2023. "Approximation Algorithms for the Capacitated Min–Max Correlation Clustering Problem," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 40(01), pages 1-13, February.
    • Handle: RePEc:wsi:apjorx:v:40:y:2023:i:01:n:s0217595922400085
    DOI: 10.1142/S0217595922400085
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