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A Comparative Performance of Sorting Algorithms: Statistical Investigation

  • PriyadarshiniEmail author
  • Anchala Kumari
Conference paper
  • 5 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1154)

Abstract

This paper is aimed at a comparative study of several sorting algorithms with the purpose of evaluating their relative performance when the input array is randomly generated from zero-truncated binomial and poisson distributions. The sorting algorithms to be investigated are quick sort, k-sort, heap sort and merge sort, all having the same average case complexity of O(NlogN). In practical statistical cases, sometimes it is not possible to record or know about the occurrences as they are limited to values which exist above or below a given limit or within a particular range. Such situations result in truncated distributions. The relative performance of the above-mentioned sorting methods was compared on the basis of ‘execution time’ and the ‘number of swapping’ for both the average and worst cases of different algorithms.

Keywords

Sorting algorithms Zero-truncated binomial distribution Zero-truncated Poisson distribution Statistical bound 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of StatisticsPatna UniversityPatnaIndia

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