A new hybrid conjugate gradient algorithm for optimization models and its application to regression analysis
Indonesian Journal of Electrical Engineering and Computer Science
; 23(2):1100-1109, 2021.
Artigo
em Inglês
| Scopus | ID: covidwho-1357659
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. © 2021 Institute of Advanced Engineering and Science. All rights reserved.
Texto completo:
Disponível
Coleções:
Bases de dados de organismos internacionais
Base de dados:
Scopus
Idioma:
Inglês
Revista:
Indonesian Journal of Electrical Engineering and Computer Science
Ano de publicação:
2021
Tipo de documento:
Artigo
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