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Toward Optimal Self-Adjusting Heaps

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Published:14 November 2017Publication History
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Abstract

We give a variant of the pairing heaps that achieves the following amortized costs: O(1) per find-min and insert, O(log log n) per decrease-key and meld, O(log n) per delete-min; where n is the number of elements in the resulting heap on which the operation is performed. These bounds are the best known for any self-adjusting heap and match two lower bounds, one established by Fredman and the other by Iacono and Özkan, for a family of self-adjusting heaps that generalizes the pairing heaps but do not include our variant. We further show how to reduce the amortized cost for meld to be paid by the other operations, on the expense of increasing that of delete-min to O(log n + log log N), where N is the total number of elements in the collection of heaps of the data structure (not just the heap under consideration by the operation).

References

  1. Amr Elmasry. 2001. Adaptive Properties of Pairing Heaps. Technical Report TR 2001-29. DIMACS.Google ScholarGoogle Scholar
  2. Amr Elmasry. 2004. Parameterized self-adjusting heaps. J. Algorithms 52, 2 (2004), 103--119. DOI:http://dx.doi.org/10.1016/j.jalgor.2004.03.002 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Amr Elmasry. 2009. Pairing heaps with O(log log n) decrease cost. In Proceedings of the 20th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA’09). 471--476. http://dl.acm.org/citation.cfm?id=1496770.1496822Google ScholarGoogle ScholarCross RefCross Ref
  4. Amr Elmasry. 2010. Pairing heaps with costless meld. In Proceedings of the 18th Annual European Symposium on Algorithms, Part II (ESA’10). 183--193. DOI:http://dx.doi.org/10.1007/978-3-642-15781-3_16 Google ScholarGoogle ScholarCross RefCross Ref
  5. Amr Elmasry and Michael L. Fredman. 1997. Unpublished experiments. (1997).Google ScholarGoogle Scholar
  6. Michael L. Fredman. 1999a. On the efficiency of pairing heaps and related data structures. J. ACM 46, 4 (1999), 473--501. DOI:http://dx.doi.org/10.1145/320211.320214 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Michael L. Fredman. 1999b. A priority queue transform. In Proceedings of the 3rd International Workshop on Algorithm Engineering, WAE’99, London, UK, July 19-21, 1999. 244--258. DOI:http://dx.doi.org/10.1007/3-540-48318-7_20 Google ScholarGoogle ScholarCross RefCross Ref
  8. Michael L. Fredman, Robert Sedgewick, Daniel Dominic Sleator, and Robert Endre Tarjan. 1986. The pairing heap: A new form of self-adjusting heap. Algorithmica 1, 1 (1986), 111--129. DOI:http://dx.doi.org/10.1007/BF01840439 Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Michael L. Fredman and Robert Endre Tarjan. 1987. Fibonacci heaps and their uses in improved network optimization algorithms. J. ACM 34, 3 (1987), 596--615. DOI:http://dx.doi.org/10.1145/28869.28874 Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. John Iacono. 2000. Improved upper bounds for pairing heaps. In Proceedings of the 7th Scandinavian Workshop on Algorithm Theory (SWAT’00). 32--45. DOI:http://dx.doi.org/10.1007/3-540-44985-X_5 Google ScholarGoogle ScholarCross RefCross Ref
  11. John Iacono and Özgür Özkan. 2014a. A tight lower bound for decrease-key in the pure heap model. CoRR abs/1407.6665 (2014). http://arxiv.org/abs/1407.6665Google ScholarGoogle Scholar
  12. John Iacono and Özgür Özkan. 2014b. Why some heaps support constant-amortized-time decrease-key operations, and others do not. In Proceedings of the 41s International Colloquium on Automata, Languages, and Programming, Part I (ICALP’14). 637--649. DOI:http://dx.doi.org/10.1007/978-3-662-43948-7_53 Google ScholarGoogle ScholarCross RefCross Ref
  13. D. Jones. 1986. An empirical comparison of priority-queues and event-set implementations. Commun. ACM 29, 4 (1986), 300--311. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Daniel H. Larkin, Siddhartha Sen, and Robert Endre Tarjan. 2014. A back-to-basics empirical study of priority queues. In Proceedings of the 16th Workshop on Algorithm Engineering and Experiments (ALENEX’14). 61--72. DOI:http://dx.doi.org/10.1137/1.9781611973198.7 Google ScholarGoogle ScholarCross RefCross Ref
  15. Bernard M. E. Moret and Henry D. Shapiro. 1992. An empirical assessment of algorithms for constructing a minimum spanning tree. In Proceedings of a DIMACS Workshop on Computational Support for Discrete Mathematics. 99--118.Google ScholarGoogle Scholar
  16. Seth Pettie. 2005. Towards a final analysis of pairing heaps. In Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05). 174--183. DOI:http://dx.doi.org/10.1109/SFCS.2005.75 Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Daniel Dominic Sleator and Robert Endre Tarjan. 1985. Self-adjusting binary search trees. J. ACM 32, 3 (1985), 652--686. DOI:http://dx.doi.org/10.1145/3828.3835 Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Daniel Dominic Sleator and Robert Endre Tarjan. 1986. Self-adjusting heaps. SIAM J. Comput. 15, 1 (1986), 52--69. DOI:http://dx.doi.org/10.1137/0215004 Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. John T. Stasko and Jeffrey Scott Vitter. 1987. Pairing heaps: Experiments and analysis. Commun. ACM 30, 3 (1987), 234--249. DOI:http://dx.doi.org/10.1145/214748.214759 Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Robert Endre Tarjan. 1985. Amortized computational complexity. SIAM J. Algebraic Discrete Methods 6 (1985), 306--318. Google ScholarGoogle ScholarDigital LibraryDigital Library

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              cover image ACM Transactions on Algorithms
              ACM Transactions on Algorithms  Volume 13, Issue 4
              October 2017
              333 pages
              ISSN:1549-6325
              EISSN:1549-6333
              DOI:10.1145/3143522
              Issue’s Table of Contents

              Copyright © 2017 ACM

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              Publication History

              • Published: 14 November 2017
              • Accepted: 1 September 2017
              • Revised: 1 June 2017
              • Received: 1 June 2016
              Published in talg Volume 13, Issue 4

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