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Optimal Facility Location under Various Distance Functions

  • Sergei Bespamyatnikh
  • Klara Kedem
  • Michael Segal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1663)

Abstract

We present efficient algorithms for two problems of facility location. In both problems we want to determine the location of a single facility with respect to n given sites. In the first we seek a location that maximizes a weighted distance function between the facility and the sites, and in the second we find a location that minimizes the sum (or sum of the squares) of the distances of k of the sites from the facility.

Keywords

Parallel Algorithm Facility Location Voronoi Diagram Query Point Rectangular Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Attalah M., Cole R., Goodrich M.: Cascading divide and conquer: a technique for designing parallel algorithms. SIAM Journal on Computing, 18(3) (1989) 499–532MathSciNetCrossRefGoogle Scholar
  2. 2.
    Aurenhammer F., Edelsbrunner H.: An optimal algorithm for for constructing the weighted Voronoi diagram in the plane. Pattern Recognition 17(2) (1984) 251–257MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Bajaj C.: Geometric optimization and computational complexity. Ph.D. thesis. Tech. Report TR 84-629. Cornell University (1984)Google Scholar
  4. 4.
    Bhattacharya B., Elgindy H.: An efficient algorithm for an intersection problem and an application. Tech. Report 86-25. Dept. of Comp. and Inform. Sci., University of Pennsylvania (1986)Google Scholar
  5. 5.
    Chazelle B.: Filtering search: A new approach to query-answering. SIAM J. Comput. 15 (1986) 703–724MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Chazelle B.: A functional approach to data structures and its use in multidimensional searching. SIAM J. Comput. 17 (1988) 427–462MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Chazelle B., Edelsbrunner H., Guibas L., Sharir M.: Algorithms for bichromatic line segment problems and polyhedral terrains. Algorithmica 11 (1994) 116–132MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Elgindy H., Keil M.: Efficient algorithms for the capacitated 1-median problem, ORSA J. Comput 4 (1982) 418–424MathSciNetGoogle Scholar
  9. 9.
    Follert F.: Lageoptimierung nach dem Maximin-Kriterium, Diploma Thesis, Univ. d. Saarlandes., Saarbrucken (1984)Google Scholar
  10. 10.
    Follert F., Schömer E., Sellen J.: Subquadratic algorithms for the weighted maximin facility location problem. in Proc. 7th Canad. Conf. Comput. Geom. (1995) 1–6Google Scholar
  11. 11.
    Megiddo N.: Applying parallel computation algorithms in the design of serial algorithms. Journal of ACM. 30 (1983) 852–865MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Megiddo N., Tamir A.: New results on the complexity of p-center problems. SIAM J. Comput. bf 12(4)(1984) 751–758MathSciNetCrossRefGoogle Scholar
  13. 13.
    Mehlhorn K.: Data Structures and Algorithms 3: Multi-dimensional Searching and Computational Geometry. Springer-Verlag (1984)Google Scholar
  14. 14.
    Preparata F.: New parallel-sorting schemes. IEEE Trans. Comput. C-27 (1978) 669–673MathSciNetCrossRefGoogle Scholar
  15. 15.
    Willard D. E., Lueker G. S.: Adding range restriction capability to dynamic data structures. in J. ACM 32 (1985) 597–617MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Sergei Bespamyatnikh
    • 1
  • Klara Kedem
    • 2
    • 3
  • Michael Segal
    • 2
  1. 1.University of British ColumbiaVancouverCanada
  2. 2.Ben-Gurion University of the NegevBeer-ShevaIsrael
  3. 3.Cornell University, Upson Hall, Cornell UniversityIthaca

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