Fast meldable priority queues

  • Gerth Stølting Brodal
Invited Presentation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 955)

Abstract

We present priority queues that support the operations Find-Min, Insert, MakeQueue and Meld in worst case time O(1) and Delete and DeleteMin in worst case time O(log n). They can be implemented on the pointer machine and require linear space. The time bounds are optimal for all implementations where Meld takes worst case time o(n).

To our knowledge this is the first priority queue implementation that supports Meld in worst case constant time and DeleteMin in logarithmic time.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    M. D. Atkinson, J.-R. Sack, N. Santoro, and T. Strothotte. Min-max heaps and generalized priority queues. Communications of the ACM, 29(10):996–1000, 1986.CrossRefGoogle Scholar
  2. 2.
    Svante Carlsson, Patricio V. Poblete, and J. Ian Munro. An implicit binomial queue with constant insertion time. In Proc. 1st Scandinavian Workshop on Algorithm Theory (SWAT), volume 318 of Lecture Notes in Computer Science, pages 1–13. Springer Verlag, Berlin, 1988.Google Scholar
  3. 3.
    Paul F. Dietz and Rajeev Raman. A constant update time finger search tree. In Advances in Computing and Information — ICCI '90, volume 468 of Lecture Notes in Computer Science, pages 100–109. Springer Verlag, Berlin, 1990.Google Scholar
  4. 4.
    Yuzheng Ding and Mark Allen Weiss. The relaxed min-max heap. ACTA Informatica, 30:215–231, 1993.CrossRefGoogle Scholar
  5. 5.
    James R. Driscoll, Harold N. Gabow, Ruth Shrairman, and Robert E. Tarjan. Relaxed heaps: An alternative to fibonacci heaps with applications to parallel computation. Communications of the ACM, 31(11):1343–1354, 1988.CrossRefGoogle Scholar
  6. 6.
    Michael J. Fischer and Michael S. Paterson. Fishspear: A priority queue algorithm. Journal of the ACM, 41(1):3–30, 1994.CrossRefGoogle Scholar
  7. 7.
    Michael L. Fredman, Robert Sedgewick, Daniel D. Sleator, and Robert E. Tarjan. The pairing heap: A new form of self-adjusting heap. Algorithmica, 1:111–129, 1986.Google Scholar
  8. 8.
    Michael L. Fredman and Robert Endre Tarjan. Fibonacci heaps and their uses in improved network optimization algorithms. In Proc. 25rd Ann. Symp. on Foundations of Computer Science (FOCS), pages 338–346, 1984.Google Scholar
  9. 9.
    Leo J. Guibas, Edward M. McCreight, Michael F. Plass, and Janet R. Roberts. A new representation for linear lists. In Proc. 9thAnn. ACM Symp. on Theory of Computing (STOC), pages 49–60, 1977.Google Scholar
  10. 10.
    Peter Høyer. A general technique for implementation of efficient priority queues. Technical Report IMADA-94-33, Odense University, 1994.Google Scholar
  11. 11.
    Robert Endre Tarjan. Amortized computational complexity. SIAM Journal on Algebraic and Discrete Methods, 6:306–318, 1985.Google Scholar
  12. 12.
    Jan van Leeuwen. The composition of fast priority queues. Technical Report RUU-CS-78-5, Department of Computer Science, University of Utrecht, 1978.Google Scholar
  13. 13.
    Jean Vuillemin. A data structure for manipulating priority queues. Communications of the ACM, 21(4):309–315, 1978.CrossRefGoogle Scholar
  14. 14.
    J. W. J. Williams. Algorithm 232: Heapsort. Communications of the ACM, 7(6):347–348, 1964.Google Scholar

Copyright information

© Springer-Verlag 1995

Authors and Affiliations

  • Gerth Stølting Brodal
    • 1
  1. 1.Basic Research in Computer Science, Centre of the Danish National Research Foundation Department of Computer ScienceUniversity of Aarhus Ny MunkegadeÅrhus CDenmark

Personalised recommendations