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An Empirical Study for Inversions-Sensitive Sorting Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 3503))

Abstract

We study the performance of the most practical internal adaptive sorting algorithms. Experimental results show that adaptive AVL sort performs the least number of comparisons unless the number of inversions is fewer than 1%. In such case, Splaysort performs the fewest number of comparisons. On the other hand, the running time of Quicksort is superior unless the number of inversions is fewer than 1.5%. In such case, Splaysort consumes the smallest running time.

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References

  1. Brodal, G., Fagerberg, R., Moruz, G.: On the adaptiveness of quicksort. In: 7th (ALENEX) Workshop on Algorithm Engineering and Experiments (2005)

    Google Scholar 

  2. Cole, R.: On the dynamic finger conjecture for splay trees. Part II: The proof. SIAM J. Computing 30, 44–85 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  3. Cook, R., Kim, J.: Best sorting algorithms for nearly sorted lists. Commun. ACM 23, 620–624 (1980)

    Article  Google Scholar 

  4. Elmasry, A.: Adaptive sorting with AVL trees. In: 3rd IFIP-WCC International Conference on Theoretical Computer Science, pp. 315–324 (2004)

    Google Scholar 

  5. Estivill-Castro, V., Wood, D.: A survey of adaptive sorting algorithms. ACM Computing Surveys 24(4), 441–476 (1992)

    Article  Google Scholar 

  6. Guibas, L., McCreight, E., Plass, M., Roberts, J.: A new representation of linear lists. 9th ACM (STOC) Symposium on Theory of Computing 9, 49–60 (1977)

    Article  MathSciNet  Google Scholar 

  7. Hoare, C.: Algorithm 64: Quicksort. Commun. ACM 4(7), 321 (1961)

    Article  Google Scholar 

  8. Levcopoulos, C., Petersson, O.: Splitsort - An adaptive sorting algorithm. Information Processing Letters 39, 205–211 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  9. Levcopoulos, C., Petersson, O.: Adaptive Heapsort. J. Alg. 14, 395–413 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  10. Moffat, A., Eddy, G., Petersson, O.: Splaysort: fast, versatile, practical. Softw. Pract. and Exper. 126(7), 781–797 (1996)

    Article  Google Scholar 

  11. Sleator, D., Tarjan, R.: Self-adjusting binary search trees. J. ACM 32(3), 652–686 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  12. Vuillemin, J.: A unifying look at data structures. Commu. ACM 23, 229–239 (1980)

    Article  MATH  MathSciNet  Google Scholar 

  13. Wainwrigh, R.: A class of sorting algorithms based on quicksort. Commun. ACM 28(4), 396–402 (1985)

    Article  Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Elmasry, A., Hammad, A. (2005). An Empirical Study for Inversions-Sensitive Sorting Algorithms. In: Nikoletseas, S.E. (eds) Experimental and Efficient Algorithms. WEA 2005. Lecture Notes in Computer Science, vol 3503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427186_52

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  • DOI: https://doi.org/10.1007/11427186_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25920-6

  • Online ISBN: 978-3-540-32078-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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