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Enumerating extreme points in higher dimensions

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STACS 95 (STACS 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 900))

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Abstract

We consider the problem of enumerating all extreme points of a given set P of n points in d dimensions. We present an algorithm with O(n) space and O(nm) time where m is the number of extreme points of P.

We also present an algorithm to compute the depth of each point of the given set of n points in d-dimensions. This algorithm has complexity O(n 2) which significantly improves the O(n 3) complexity of the previously best known deterministic algorithm. It also improves the best known randomized algorithm which has a expected running time of \(O(n^{3 - \frac{2}{{1 + [d/2]}} + \delta } )\) (for any fixed δ>0).

This research is supported by the DFG-Project “Diskrete Probleme”, No. Ot 64/8-1.

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References

  1. P. Agarwal and J. Matousek. Ray shooting and parametric search. In Proc. 21st ACM Symp. on Theory of Computing, pages 517–526, 1992.

    Google Scholar 

  2. S. G. Akl and G. T. Toussaint. A fast convex hull algorithm. Information Processing Letters, 7(5):219–222, 1978.

    Google Scholar 

  3. B. K. Bhattacharya and G. T. Toussaint. Time-and storage-efficient implementation of an optimal convex hull algorithm. Image Vision Computing, 1:140–144, 1983.

    Google Scholar 

  4. K. H. Borgwardt, N. Gaffke, M. Jünger, and G. Reinelt. Computing the convex hull in the euclidean plane in linear expected time. In Applied Geometry and Discrete Mathematics THE VICTOR KLEE FESTSCHRIFT, pages 91–107. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Volume 4, 1991.

    Google Scholar 

  5. B. Chazelle. An optimal convex hull algorithm and new results on cuttings. In Proc. 32nd IEEE Symp. on Foundations of Computer Science, pages 29–38, 1991.

    Google Scholar 

  6. B. Chazelle. Optimal algorithms for computing depths and layers. In Proc. 21st Allerton Conference on Communication, Control and Computation, pages 427–436, 1983.

    Google Scholar 

  7. V. Chvátal. Linear Programming. W. H. Freeman, New York, NY, 1983.

    Google Scholar 

  8. K. L. Clarkson. A Las Vegas algorithm for linear programming when the dimension is small. In Proc. 29th IEEE Symp. on Foundations of Computer Science, pages 452–456, 1988.

    Google Scholar 

  9. D. P. Dobkin and S. P. Reiss. The complexity of linear programming. Theoretical Computer Science, 11:1–18, 1980.

    Google Scholar 

  10. M. E. Dyer. Linear time algorithms for two-and three-variable linear programs. SIAM Journal of Computing, 13:31–45, 1984.

    Google Scholar 

  11. H. Edelsbrunner and W. Shi. An O(n log2 h) time algorithm for the three-dimensional convex hull problem. SIAM Journal of Computing, 20:259–277, 1991.

    Google Scholar 

  12. H. Edelsbrunner. Algorithms in Combinatorial Geometry. Springer Verlag, EATCS Monographs on Theoretical Computer Science Edition, 1987.

    Google Scholar 

  13. R. L. Graham. An efficient algorithm for determining the convex hull of a finite planar set. Information Processing Letters, 1:132–133, 1972.

    Google Scholar 

  14. G. H. Johansen and C. Gram. A simple algorithm for building the 3-d convex hull. BIT, 23:146–160, 1983.

    Google Scholar 

  15. M. Kallay. Convex hull made easy. Information Processing Letters, 22:161–163, 1986.

    Google Scholar 

  16. D. G. Kirkpatrick and R. Seidel. The ultimate planar convex hull algorithm? SIAM Journal of Computing, 15:287–299, 1986.

    Google Scholar 

  17. J. Matousek and O. Schwarzkopf. Linear optimization queries. In Proc. 8th ACM Symp. on Computational Geometry, pages 16–25, 1992.

    Google Scholar 

  18. N. Megiddo. Linear programming in linear time when the dimension is fixed. Journal of the ACM, 31:114–127, 1984.

    Google Scholar 

  19. M. Overmars and J. van Leeuwen. Maintenance of configurations in the plane. Journal of Computer and System Sciences, 23:166–204, 1981.

    Google Scholar 

  20. E. Welzl and R. Seidel, referred to in [17], p. 25.

    Google Scholar 

  21. A. Rényi and R. Sulanke. Über die konvexe Hülle von n zufällig gewählten Punkten. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete, 2:75–84, 1963.

    Google Scholar 

  22. R. Seidel. Small-dimensional linear programming and convex hulls made easy. Discrete & Computational Geometry, 6:423–434, 1991.

    Google Scholar 

  23. M. Sharir and E. Welzl. A combinatorial bound for linear programming and related problems. In Proc. 9th Symp. on Theoretical Aspects Computer Science, LNCS 577, pages 569–579. Springer-Verlag, 1992.

    Google Scholar 

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Ernst W. Mayr Claude Puech

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© 1995 Springer-Verlag Berlin Heidelberg

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Ottmann, T., Schuierer, S., Soundaralakshmi, S. (1995). Enumerating extreme points in higher dimensions. In: Mayr, E.W., Puech, C. (eds) STACS 95. STACS 1995. Lecture Notes in Computer Science, vol 900. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59042-0_105

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  • DOI: https://doi.org/10.1007/3-540-59042-0_105

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  • Print ISBN: 978-3-540-59042-2

  • Online ISBN: 978-3-540-49175-0

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