Open AccessA new hybrid conjugate gradient algorithm for optimization models and its application to regression analysis
Author(s) -
Ibrahim Mohammed Sulaiman,
Norsuhaily Abu Bakar,
Mustafa Mamat,
Basim A. Hassan,
Maulana Malik,
Alomari Mohammad Ahmed
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v23.i2.pp1100-1109
Subject(s) - conjugate gradient method , convergence (economics) , nonlinear conjugate gradient method , gradient descent , parameterized complexity , line search , algorithm , mathematical optimization , regression , mathematics , computer science , artificial neural network , statistics , artificial intelligence , radius , computer security , economics , economic growth
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.