Wikipedia

Template:Heap Running Times/doc

< Template:Heap Running Times

Objective

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{{Heap Running Times}} provides time complexity information for operations across different types of heaps.

Usage

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{{Heap Running Times |mode = min}}

where

mode
optional parameter. If present and set to "max", present information for max heap; otherwise present for min heap

Examples

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Here are time complexities[1] of various heap data structures. The abbreviation am. indicates that the given complexity is amortized, otherwise it is a worst-case complexity. For the meaning of "O(f)" and "Θ(f)" see Big O notation. Names of operations assume a min-heap.

Operation find-min delete-min decrease-key insert meld make-heap[a]
Binary[1] Θ(1) Θ(log n) Θ(log n) Θ(log n) Θ(n) Θ(n)
Skew[2] Θ(1) O(log n) am. O(log n) am. O(log n) am. O(log n) am. Θ(n) am.
Leftist[3] Θ(1) Θ(log n) Θ(log n) Θ(log n) Θ(log n) Θ(n)
Binomial[1][5] Θ(1) Θ(log n) Θ(log n) Θ(1) am. Θ(log n)[b] Θ(n)
Skew binomial[6] Θ(1) Θ(log n) Θ(log n) Θ(1) Θ(log n)[b] Θ(n)
2–3 heap[8] Θ(1) O(log n) am. Θ(1) Θ(1) am. O(log n)[b] Θ(n)
Bottom-up skew[2] Θ(1) O(log n) am. O(log n) am. Θ(1) am. Θ(1) am. Θ(n) am.
Pairing[9] Θ(1) O(log n) am. o(log n) am.[c] Θ(1) Θ(1) Θ(n)
Rank-pairing[12] Θ(1) O(log n) am. Θ(1) am. Θ(1) Θ(1) Θ(n)
Fibonacci[1][13] Θ(1) O(log n) am. Θ(1) am. Θ(1) Θ(1) Θ(n)
Strict Fibonacci[14][d] Θ(1) Θ(log n) Θ(1) Θ(1) Θ(1) Θ(n)
Brodal[15][d] Θ(1) Θ(log n) Θ(1) Θ(1) Θ(1) Θ(n)[16]
  1. ^ make-heap is the operation of building a heap from a sequence of n unsorted elements. It can be done in Θ(n) time whenever meld runs in O(log n) time (where both complexities can be amortized).[2][3] Another algorithm achieves Θ(n) for binary heaps.[4]
  2. ^ a b c For persistent heaps (not supporting decrease-key), a generic transformation reduces the cost of meld to that of insert, while the new cost of delete-min is the sum of the old costs of delete-min and meld.[7] Here, it makes meld run in Θ(1) time (amortized, if the cost of insert is) while delete-min still runs in O(log n). Applied to skew binomial heaps, it yields Brodal-Okasaki queues, persistent heaps with optimal worst-case complexities.[6]
  3. ^ Lower bound of [10] upper bound of [11]
  4. ^ a b Brodal queues and strict Fibonacci heaps achieve optimal worst-case complexities for heaps. They were first described as imperative data structures. The Brodal-Okasaki queue is a persistent data structure achieving the same optimum, except that decrease-key is not supported.
  1. ^ a b c d Cormen, Thomas H.; Leiserson, Charles E.; Rivest, Ronald L. (1990). Introduction to Algorithms (1st ed.). MIT Press and McGraw-Hill. ISBN 0-262-03141-8.
  2. ^ a b c Sleator, Daniel Dominic; Tarjan, Robert Endre (February 1986). "Self-Adjusting Heaps". SIAM Journal on Computing. 15 (1): 52–69. CiteSeerX 10.1.1.93.6678. doi:10.1137/0215004. ISSN 0097-5397.
  3. ^ a b Tarjan, Robert (1983). "3.3. Leftist heaps". Data Structures and Network Algorithms. pp. 38–42. doi:10.1137/1.9781611970265. ISBN 978-0-89871-187-5.
  4. ^ Hayward, Ryan; McDiarmid, Colin (1991). "Average Case Analysis of Heap Building by Repeated Insertion" (PDF). J. Algorithms. 12: 126–153. CiteSeerX 10.1.1.353.7888. doi:10.1016/0196-6774(91)90027-v. Archived from the original (PDF) on 2016-02-05. Retrieved 2016-01-28.
  5. ^ "Binomial Heap | Brilliant Math & Science Wiki". brilliant.org. Retrieved 2019-09-30.
  6. ^ a b Brodal, Gerth Stølting; Okasaki, Chris (November 1996), "Optimal purely functional priority queues", Journal of Functional Programming, 6 (6): 839–857, doi:10.1017/s095679680000201x
  7. ^ Okasaki, Chris (1998). "10.2. Structural Abstraction". Purely Functional Data Structures (1st ed.). pp. 158–162. ISBN 9780521631242.
  8. ^ Takaoka, Tadao (1999), Theory of 2–3 Heaps (PDF), p. 12
  9. ^ Iacono, John (2000), "Improved upper bounds for pairing heaps", Proc. 7th Scandinavian Workshop on Algorithm Theory (PDF), Lecture Notes in Computer Science, vol. 1851, Springer-Verlag, pp. 63–77, arXiv:1110.4428, CiteSeerX 10.1.1.748.7812, doi:10.1007/3-540-44985-X_5, ISBN 3-540-67690-2
  10. ^ Fredman, Michael Lawrence (July 1999). "On the Efficiency of Pairing Heaps and Related Data Structures" (PDF). Journal of the Association for Computing Machinery. 46 (4): 473–501. doi:10.1145/320211.320214.
  11. ^ Pettie, Seth (2005). Towards a Final Analysis of Pairing Heaps (PDF). FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science. pp. 174–183. CiteSeerX 10.1.1.549.471. doi:10.1109/SFCS.2005.75. ISBN 0-7695-2468-0.
  12. ^ Haeupler, Bernhard; Sen, Siddhartha; Tarjan, Robert E. (November 2011). "Rank-pairing heaps" (PDF). SIAM J. Computing. 40 (6): 1463–1485. doi:10.1137/100785351.
  13. ^ Fredman, Michael Lawrence; Tarjan, Robert E. (July 1987). "Fibonacci heaps and their uses in improved network optimization algorithms" (PDF). Journal of the Association for Computing Machinery. 34 (3): 596–615. CiteSeerX 10.1.1.309.8927. doi:10.1145/28869.28874.
  14. ^ Brodal, Gerth Stølting; Lagogiannis, George; Tarjan, Robert E. (2012). Strict Fibonacci heaps (PDF). Proceedings of the 44th symposium on Theory of Computing - STOC '12. pp. 1177–1184. CiteSeerX 10.1.1.233.1740. doi:10.1145/2213977.2214082. ISBN 978-1-4503-1245-5.
  15. ^ Brodal, Gerth S. (1996), "Worst-Case Efficient Priority Queues" (PDF), Proc. 7th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 52–58
  16. ^ Goodrich, Michael T.; Tamassia, Roberto (2004). "7.3.6. Bottom-Up Heap Construction". Data Structures and Algorithms in Java (3rd ed.). pp. 338–341. ISBN 0-471-46983-1.
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