On List Update with Locality of Reference

  • Susanne Albers
  • Sonja Lauer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5125)


We present a comprehensive study of the list update problem with locality of reference. More specifically, we present a combined theoretical and experimental study in which the theoretically proven and experimentally observed performance guarantees of algorithms match or nearly match. In the first part of the paper we introduce a new model of locality of reference that is based on the natural concept of runs. Using this model we develop refined theoretical analyses of popular list update algorithms. The second part of the paper is devoted to an extensive experimental study in which we have tested the algorithms on traces from benchmark libraries. It shows that the theoretical and experimental bounds differ by just a few percent. Our new bounds are substantially lower than those provided by standard competitive analysis. Another result is that the elegant Move-To-Front strategy exhibits the best performance, which confirms that it is the method of choice in practice.


Competitive Ratio Online Algorithm Performance Ratio Average Relative Error Theoretical Bound 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Albers, S.: Improved randomized on-line algorithms for the list update problem. SIAM Journal on Computing 27, 670–681 (1998)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Albers, S., Favrholdt, L.M., Giel, O.: On paging with locality of reference. Journal of Computer and System Sciences 70, 145–175 (2005)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Albers, S., von Stengel, B., Werchner, R.: A combined BIT and TIMESTAMP algorithm for the list update problem. Information Processing Letters 56, 135–139 (1995)CrossRefMATHGoogle Scholar
  4. 4.
    Ambühl, C.: Offline List update is NP-hard. In: Paterson, M. (ed.) ESA 2000. LNCS, vol. 1879, pp. 42–51. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Ambühl, C., Gärtner, B., von Stengel, B.: A new lower bound for the list update problem in the partial cost model. Theoretical Computer Science 268, 3–16 (2001)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Angelopoulos, S., Dorrigiv, R., López-Ortiz, A.: List update with locality of reference: MTF outperforms all other algorithms. Technical Report CS-2006-46, School of Computer Science, University of Waterloo (2006)Google Scholar
  7. 7.
    Bachrach, R., El-Yaniv, R.: Online list accessing algorithms and their applications: Recent empirical evidence. In: Proc. 8th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 53–62 (1997)Google Scholar
  8. 8.
    Bachrach, R., El-Yaniv, R., Reinstädtler, M.: On the competitive theory and practice of online list accessing algorithms. Algorithmica 32, 201–245 (2002)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Bentley, J.L., McGeoch, C.C.: Amortized analyses of self-organizing sequential search heuristics. Communication of the ACM 28, 404–411 (1985)CrossRefGoogle Scholar
  10. 10.
    Bentley, J.L., Sleator, D.S., Tarjan, R.E., Wei, V.K.: A locally adaptive data compression scheme. Communications of the ACM 29, 320–330 (1986)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Borodin, A., Irani, S., Raghavan, P., Schieber, B.: Competitive paging with locality of reference. Journal of Computer and System Sciences 50, 244–258 (1995)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Burrows, M., Wheeler, D.J.: A block-sorting lossless data compression algorithm. DEC SRC Research Report 124 (1994)Google Scholar
  13. 13.
  14. 14.
    The Canterbury Corpus, http://corpus.canterbury.ac.nz/
  15. 15.
    Fiat, A., Mendel, M.: Truly online paging with locality of reference. In: Proc. 38rd Annual Symposium on Foundations of Computer Science, pp. 326–335 (1997)Google Scholar
  16. 16.
    Hester, J.H., Hirschberg, D.S.: Self-organizing linear search. ACM Computing Surveys 17, 295–312 (1985)CrossRefGoogle Scholar
  17. 17.
    Irani, S.: Two results on the list update problem. Information Processing Letters 38, 301–306 (1991)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Karlin, A., Phillips, S., Raghavan, P.: Markov paging. In: Proc. 33rd Annual Symposium on Foundations of Computer Science, pp. 24–27 (1992)Google Scholar
  19. 19.
    Karp, R., Raghavan, P.: Personal communication cisted in [22] (1990)Google Scholar
  20. 20.
    Koutsoupias, E., Papadimitriou, C.H.: Beyond competitive analysis. In: Proc. 35th Annual Symposium on Foundations of Computer Science, pp. 394–400 (1994)Google Scholar
  21. 21.
    Reingold, N., Westbrook, J.: Optimum off-line algorithms for the list update problem. Technical Report YALEU/DCS/TR-805, Yale University (1990)Google Scholar
  22. 22.
    Reingold, N., Westbrook, J., Sleator, D.D.: Randomized competitive algorithms for the list update problem. Algorithmica 11, 15–32 (1994)MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Rivest, R.: On self-organizing sequential search heuristics. Communications of the ACM 19, 63–67 (1976)MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Sleator, D.D., Tarjan, R.E.: Amortized efficiency of list update and paging rules. Communications of the ACM 28, 202–208 (1985)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Susanne Albers
    • 1
  • Sonja Lauer
    • 1
  1. 1.University of FreiburgFreiburgGermany

Personalised recommendations