Skip to main content
Log in

An approximation algorithm for k-level squared metric facility location problem with outliers

  • Original Paper
  • Published:
Optimization Letters Aims and scope Submit manuscript

Abstract

We investigate k-level squared metric facility location problem with outliers (k-SMFLPWO) for any constant k. In k-SMFLPWO, given k facilities set \({\mathcal {F}}_{l}\), where \(l\in \{1, 2, \cdots , k\}\), clients set \({\mathcal {C}}\) with cardinality n and a non-negative integer \(q<n\). The sum of opening and connection cost will be substantially increased by distant clients. To minimize the total cost, some distant clients can not be connected, in short, at least \(n-q\) clients in clients set \({\mathcal {C}}\) are connected to the path \(p=(i_{1}\in {\mathcal {F}}_{1}, i_{2}\in {\mathcal {F}}_{2}, \cdots , i_{k}\in {\mathcal {F}}_{k})\) where the facilities in path p are opened. Based on primal-dual approximation algorithm and the property of squared metric triangle inequality, we present a constant factor approximation algorithm for k-SMFLPWO.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Song, H.O., Jegelka, S., Rathod, V., Murphy, K.: Deep metric learning via facility location. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition. CVPR, pp. 2206–2214. IEEE Computer Society, Honolulu, HI, USA (2017)

  2. Teo, C., Shu, J.: Warehouse-retailer network design problem. Oper. Res. 52(3), 396–408 (2004)

    Article  MathSciNet  Google Scholar 

  3. Wang, X., Hua, Y., Kodirov, E., Robertson, N.M.: Ranked list loss for deep metric learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5414–5429 (2022)

    Google Scholar 

  4. Chudak, F.A., Shmoys, D.B.: Improved approximation algorithms for the uncapacitated facility location problem. SIAM J. Comput. 33(1), 1–25 (2003)

    Article  MathSciNet  Google Scholar 

  5. Mahdian, M., Ye, Y., Zhang, J.: Approximation algorithms for metric facility location problems. SIAM J. Comput. 36(2), 411–432 (2006)

    Article  MathSciNet  Google Scholar 

  6. Sviridenko, M.: An improved approximation algorithm for the metric uncapacitated facility location problem. In: Integer Programming and Combinatorial Optimization, 9th International IPCO Conference. Lecture Notes in Computer Science, vol. 2337, pp. 240–257. Springer, Cambridge, MA, USA (2002)

  7. Shmoys, D.B., Tardos, É., Aardal, K.: Approximation algorithms for facility location problems (extended abstract). In: Proceedings of the Twenty-Ninth Annual ACM Symposium on the Theory of Computing, pp. 265–274. ACM, El Paso, Texas, USA (1997)

  8. Lin, J., Vitter, J.S.: ε-approximations with minimum packing constraint violation (extended abstract). In: Proceedings of the 24th Annual ACM Symposium on Theory of Computing, pp. 771–782. ACM, Victoria, British Columbia, Canada (1992)

  9. Guha, S., Khuller, S.: Greedy strikes back: improved facility location algorithms. J. Algorithms 31(1), 228–248 (1999)

    Article  MathSciNet  Google Scholar 

  10. Li, S.: A 1.488 approximation algorithm for the uncapacitated facility location problem. Inform. Comput. 222, 45–58 (2013)

    Article  MathSciNet  Google Scholar 

  11. Charikar, M., Khuller, S., Mount, D.M., Narasimhan, G.: Algorithms for facility location problems with outliers. In: Proceedings of the Twelfth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 642–651. SIAM, Philadelphia, PA (2001)

  12. Han, L., Xu, D., Liu, D., Wu, C.: An approximation algorithm for the k-level facility location problem with outliers. Optim. Lett. 15(6), 2053–2065 (2021)

    Article  MathSciNet  Google Scholar 

  13. Wang, Y., Zhang, D., Zhang, P., Zhang, Y.: Local search algorithm for the squared metric k-facility location problem with linear penalties. J. Ind. Manag. Optim. 17(4), 2013–2030 (2021)

    Article  MathSciNet  Google Scholar 

  14. Zhang, Z., Feng, Q.: An improved approximation algorithm for squared metric k-facility location. In: Combinatorial Optimization and Applications–15th International Conference. Lecture Notes in Computer Science, vol. 13135, pp. 538–552. Springer, Tianjin, China (2021)

  15. Fernandes, C.G., Meira, L.A.A., Miyazawa, F.K., Pedrosa, L.L.C.: A systematic approach to bound factor-revealing LPs and its application to the metric and squared metric facility location problems. Math. Program. 153(2), 655–685 (2015)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

We would like to thank two anonymous referees for their constructive comments. This work is supported by National Natural Science Foundation of China (No. 12071126).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qiaoliang Li.

Ethics declarations

Conflict of interest

The authors declare that they have no Conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Yuan, J. & Li, Q. An approximation algorithm for k-level squared metric facility location problem with outliers. Optim Lett (2024). https://doi.org/10.1007/s11590-024-02107-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11590-024-02107-y

Keywords

Navigation