The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Outstanding Reviewers

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • ICONS_BA 2025

Computer Vision Conference (CVC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • RSS Feed

DOI: 10.14569/IJACSA.2019.0100344
PDF

An Effective Approach to Analyze Algorithms with Linear O(n) Worst-Case Asymptotic Complexity

Author 1: Qazi Haseeb Yousaf
Author 2: Muhammad Arif Shah
Author 3: Rashid Naseem
Author 4: Karzan Wakil
Author 5: Ghufran Ullah

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 3, 2019.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: A theoretical approach of asymptote analyzes the algorithms for approximate time complexity. The worst-case asymptotic complexity classifies an algorithm to a certain class. The asymptotic complexity for algorithms returns the degree variable of the algorithmic function while ignores the lower terms. In perspective of programming, asymptote only considers the number of iterations in a loop ignoring inside and outside statements. However, every statement must have some execution time. This paper provides an effective approach to analyze the algorithms belonging to the same class of asymptotes. The theoretical analysis of algorithmic functions shows that the difference between theoretical outputs of two algorithmic functions depends upon the difference between their coefficient of ‘n’ and the constant term. The said difference marks the point for the behavioral change of algorithms. This theoretic analysis approach is applied to algorithms with linear asymptotic complexity. Two algorithms are considered having a different number of statements outside and inside the loop. The results positively indicated the effectiveness of the proposed approach as the tables and graphs validates the results of the derived formula.

Keywords: Asymptotic complexity; interval analysis; in-depth analysis; Big-Oh; crossover point

Qazi Haseeb Yousaf, Muhammad Arif Shah, Rashid Naseem, Karzan Wakil and Ghufran Ullah. “An Effective Approach to Analyze Algorithms with Linear O(n) Worst-Case Asymptotic Complexity”. International Journal of Advanced Computer Science and Applications (IJACSA) 10.3 (2019). http://dx.doi.org/10.14569/IJACSA.2019.0100344

@article{Yousaf2019,
title = {An Effective Approach to Analyze Algorithms with Linear O(n) Worst-Case Asymptotic Complexity},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100344},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100344},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Qazi Haseeb Yousaf and Muhammad Arif Shah and Rashid Naseem and Karzan Wakil and Ghufran Ullah}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

Computer Vision Conference (CVC) 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

Artificial Intelligence Conference 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computer Vision Conference
  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

The Science and Information (SAI) Organization Limited is a company registered in England and Wales under Company Number 8933205.