TY - JOUR
T1 - A new hybrid conjugate gradient algorithm for optimization models and its application to regression analysis
AU - Sulaiman, Ibrahim Mohammed
AU - Bakar, Norsuhaily Abu
AU - Mamat, Mustafa
AU - Hassan, Basim A.
AU - Malik, Maulana
AU - Ahmed, Alomari Mohammad
N1 - Funding Information:
The authors are grateful to Universiti Sultan Zainal Abidin, Malaysia, for funding this research.
Publisher Copyright:
© 2021 Institute of Advanced Engineering and Science. All rights reserved.
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
KW - Conjugate gradient method
KW - Convergence analysis
KW - Line search procedures
KW - Regression analysis
UR - http://www.scopus.com/inward/record.url?scp=85112029580&partnerID=8YFLogxK
U2 - 10.11591/ijeecs.v23.i2.pp1100-1109
DO - 10.11591/ijeecs.v23.i2.pp1100-1109
M3 - Article
AN - SCOPUS:85112029580
SN - 2502-4752
VL - 23
SP - 1100
EP - 1109
JO - Indonesian Journal of Electrical Engineering and Computer Science
JF - Indonesian Journal of Electrical Engineering and Computer Science
IS - 2
ER -