Comprehensive Analysis of Counting by Sorting


Year : 2024 | Volume : 11 | Issue : 02 | Page : 1-6
    By

    Mahammad Akhil,

  • Husensab,

  • Mohamed Rafi,

Abstract

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_181905’);});Edit Abstract & Keyword

Sorting algorithms play an important role in computer science, as they facilitate the effective organization and retrieval of data. Counting sort is a non-comparative integer sorting algorithm that works well with a limited number of integers known beforehand. The process, advantages, and limitations of this sorting algorithm were investigated in this study. Unlike other comparison-based sorting algorithms, counting sort achieves a time complexity of O(n+k), which depends on the input data range. Here, k represents the input range, and n is the size of the input array. Through thorough studies and experimental evaluations, we demonstrate that counting sort is superior to more traditional algorithms, such as quicksort and mergesort, when working within limited data ranges. We also analyze space complexity as well as its implications for large datasets. These findings indicate the significance of counting sort in various applications, such as radix sorting, and its possible inclusion into hybrid sorts. Thus, this study intends to provide comprehensive insight into how to enhance the counting sort’s usability across different computational environments, and despite its challenges, counting sort continues to be a highly valuable sorting algorithm, providing exceptional speed and efficiency when applied under suitable conditions. This research also investigates the possibility of integrating the counting sort into hybrid sorting algorithms, aiming to leverage its strengths while minimizing its limitations. The findings of this study offer key insights into improving the usability of counting sort across various computational settings, highlighting its significance in specialized applications and potential for wider adoption in more complex sorting scenarios.

Keywords: Counting sort, efficiency algorithm, running time, radix sort, algorithm optimization

[This article belongs to Research & Reviews: Discrete Mathematical Structures ]

How to cite this article:
Mahammad Akhil, Husensab, Mohamed Rafi. Comprehensive Analysis of Counting by Sorting. Research & Reviews: Discrete Mathematical Structures. 2024; 11(02):1-6.
How to cite this URL:
Mahammad Akhil, Husensab, Mohamed Rafi. Comprehensive Analysis of Counting by Sorting. Research & Reviews: Discrete Mathematical Structures. 2024; 11(02):1-6. Available from: https://journals.stmjournals.com/rrdms/article=2024/view=0


document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_181905’);});Edit

References

  1. Knuth DE. The art of computer programming. Sorting and searching. 3rd ed. Reading (USA): Addison-Wesley; 1998.
  2. Cormen TH, Leiserson CE, Rivest RL, Stein C. Introduction to Algorithms. 4th ed. Cambridge (USA): MIT Press; 2022.
  3. Sedgewick R, Wayne K. Algorithms. Reading (USA): Addison-Wesley Professional; 2011.
  4. Weiss MA. Data structures and algorithm analysis in C++. 4th ed. London (UK): Pearson; 2012.
  5. Goodrich MT, Tamassia R. Algorithm design and applications. Chichester (UK): Wiley; 2014.
  6. Galil Z. Efficient algorithms for finding maximum matching in graphs. ACM Comput Surv. 1986;18:23–38. DOI: 10.1145/6462.6502.
  7. Ball MO, Derigs U. An analysis of alternative strategies for implementing matching algorithms. Networks. 1983;13:517–49. DOI: 10.1002/net.3230130406.
  8. Lozin VV. On maximum induced matchings in bipartite graphs. Inf Process Lett. 2002;81:7–11. DOI: 10.1016/S0020-0190(01)00185-5.
  9. Mehlhorn K. Data structures and algorithms 1: sorting and searching. New York (USA): Springer Science+Business Media; 2013.
  10. Wulf WA, Flon L, Shaw M, Hilfinger P. Fundamental structures of computer science. Addison (USA): Wesley Longman Publishing Co, Inc.; 1981.
Regular Issue Subscription Original Research
Volume 11
Issue 02
Received 05/08/2024
Accepted 30/08/2024
Published 30/08/2024
Publication Time 25 Days
80

async function fetchCitationCount(doi) {
let apiUrl = `https://api.crossref.org/works/${doi}`;
try {
let response = await fetch(apiUrl);
let data = await response.json();
let citationCount = data.message[“is-referenced-by-count”];
document.getElementById(“citation-count”).innerText = `Citations: ${citationCount}`;
} catch (error) {
console.error(“Error fetching citation count:”, error);
document.getElementById(“citation-count”).innerText = “Citations: Data unavailable”;
}
}
fetchCitationCount(“10.37591/RRDMS.v11i02.0”);

Loading citations…

PlumX Metrics