A new hybrid conjugate gradient algorithm for optimization models and its application to regression analysis

Ibrahim Mohammed Sulaiman, Norsuhaily Abu Bakar, Mustafa Mamat, Basim A. Hassan, Maulana Malik, Alomari Mohammad Ahmed

Research output: Contribution to journalArticlepeer-review

Abstract

The hybrid conjugate gradient (CG) method is among the efficient variants of CG method for solving optimization problems. This is due to their low memory requirements and nice convergence properties. In this paper, we present an efficient hybrid CG method for solving unconstrained optimization models and show that the method satisfies the sufficient descent condition. The global convergence prove of the proposed method would be established under inexact line search. Application of the proposed method to the famous statistical regression model describing the global outbreak of the novel COVID-19 is presented. The study parameterized the model using the weekly increase/decrease of recorded cases from December 30, 2019 to March 30, 2020. Preliminary numerical results on some unconstrained optimization problems show that the proposed method is efficient and promising. Furthermore, the proposed method produced a good regression equation for COVID-19 confirmed cases globally.

Original languageEnglish
Pages (from-to)1100-1109
Number of pages10
JournalIndonesian Journal of Electrical Engineering and Computer Science
Volume23
Issue number2
DOIs
Publication statusPublished - Aug 2021

Keywords

  • Conjugate gradient method
  • Convergence analysis
  • Line search procedures
  • Regression analysis

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