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Online Unit Clustering in Higher Dimensions

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Approximation and Online Algorithms (WAOA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10787))

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Abstract

We revisit the online Unit Clustering problem in higher dimensions: Given a set of n points in \(\mathbb {R}^d\), that arrive one by one, partition the points into clusters (subsets) of diameter at most one, so as to minimize the number of clusters used. In this paper, we work in \(\mathbb {R}^d\) using the \(L_\infty \) norm. We show that the competitive ratio of any algorithm (deterministic or randomized) for this problem must depend on the dimension d. This resolves an open problem raised by Epstein and van Stee (WAOA 2008). We also give a randomized online algorithm with competitive ratio \(O(d^2)\) for Unit Clustering of integer points (i.e., points in \(\mathbb {Z}^d\), \(d\in \mathbb {N}\), under \(L_{\infty }\) norm). We complement these results with some additional lower bounds for related problems in higher dimensions.

Research supported in part by the NSF awards CCF-1422311 and CCF-1423615.

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References

  1. Alon, N., Awerbuch, B., Azar, Y., Buchbinder, N., Naor, J.: The online set cover problem. SIAM J. Comput. 39(2), 361–370 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  2. Azar, Y., Buchbinder, N., Hubert Chan, T.-H., Chen, S., Cohen, I.R., Gupta, A., Huang, Z., Kang, N., Nagarajan, V., Naor, J., Panigrahi, D.: Online algorithms for covering and packing problems with convex objectives. In: Proceedings of the 57th IEEE Symposium on Foundations of Computer Science (FOCS), pp. 148–157. IEEE (2016)

    Google Scholar 

  3. Azar, Y., Bhaskar, U., Fleischer, L., Panigrahi, D.: Online mixed packing and covering. In: Proceedings of the 24th ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 85–100. SIAM (2013)

    Google Scholar 

  4. Azar, Y., Cohen, I.R., Roytman, A.: Online lower bounds via duality. In: Proceedings of the 28th ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 1038–1050. SIAM (2017)

    Google Scholar 

  5. Borodin, A., El-Yaniv, R.: Online Computation and Competitive Analysis. Cambridge University Press, Cambridge (1998)

    MATH  Google Scholar 

  6. Buchbinder, N., Naor, J.: Online primal-dual algorithms for covering and packing. Math. Oper. Res. 34(2), 270–286 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  7. Chan, T.M., Zarrabi-Zadeh, H.: A randomized algorithm for online unit clustering. Theory Comput. Syst. 45(3), 486–496 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  8. Charikar, M., Chekuri, C., Feder, T., Motwani, R.: Incremental clustering and dynamic information retrieval. SIAM J. Comput. 33(6), 1417–1440 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  9. Chrobak, M.: SIGACT news online algorithms column 13. SIGACT News Bull. 39(3), 96–121 (2008)

    Article  Google Scholar 

  10. Csirik, J., Epstein, L., Imreh, C., Levin, A.: Online clustering with variable sized clusters. Algorithmica 65(2), 251–274 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  11. Divéki, G., Imreh, C.: An online 2-dimensional clustering problem with variable sized clusters. Optim. Eng. 14(4), 575–593 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  12. Divéki, G., Imreh, C.: Grid based online algorithms for clustering problems. In. Proceedings of the 15th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), p. 159. IEEE (2014)

    Google Scholar 

  13. Ehmsen, M.R., Larsen, K.S.: Better bounds on online unit clustering. Theor. Comput. Sci. 500, 1–24 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  14. Epstein, L., Levin, A., van Stee, R.: Online unit clustering: variations on a theme. Theor. Comput. Sci. 407(1–3), 85–96 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  15. Epstein, L., van Stee, R.: On the online unit clustering problem. ACM Trans. Algorithms 7(1), 1–18 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  16. Feder, T., Greene, D.H.: Optimal algorithms for approximate clustering. In: Proceedings of the 20th Annual ACM Symposium on Theory of Computing (STOC), pp. 434–444 (1988)

    Google Scholar 

  17. Fowler, R.J., Paterson, M., Tanimoto, S.L.: Optimal packing and covering in the plane are NP-complete. Inf. Process. Lett. 12(3), 133–137 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  18. Gonzalez, T.F.: Clustering to minimize the maximum intercluster distance. Theor. Comput. Sci. 38, 293–306 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  19. Gupta, A., Nagarajan, V.: Approximating sparse covering integer programs online. Math. Oper. Res. 39(4), 998–1011 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  20. Hochbaum, D.S., Maass, W.: Approximation schemes for covering and packing problems in image processing and VLSI. J. ACM 32(1), 130–136 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  21. Kawahara, J., Kobayashi, K.M.: An improved lower bound for one-dimensional online unit clustering. Theor. Comput. Sci. 600, 171–173 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  22. Megiddo, N., Supowit, K.J.: On the complexity of some common geometric location problems. SIAM J. Comput. 13(1), 182–196 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  23. Vazirani, V.: Approximation Algorithms. Springer, New York (2001). https://doi.org/10.1007/978-3-662-04565-7

    MATH  Google Scholar 

  24. Williamson, D.P., Shmoys, D.B.: The Design of Approximation Algorithms. Cambridge University Press, Cambridge (2011)

    Book  MATH  Google Scholar 

  25. Zarrabi-Zadeh, H., Chan, T.M.: An improved algorithm for online unit clustering. Algorithmica 54(4), 490–500 (2009)

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Csaba D. Tóth .

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Dumitrescu, A., Tóth, C.D. (2018). Online Unit Clustering in Higher Dimensions. In: Solis-Oba, R., Fleischer, R. (eds) Approximation and Online Algorithms. WAOA 2017. Lecture Notes in Computer Science(), vol 10787. Springer, Cham. https://doi.org/10.1007/978-3-319-89441-6_18

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  • DOI: https://doi.org/10.1007/978-3-319-89441-6_18

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