TY - JOUR
T1 - On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control
AU - Sulaiman, Ibrahim Mohammed
AU - Malik, Maulana
AU - Awwal, Aliyu Muhammed
AU - Kumam, Poom
AU - Mamat, Mustafa
AU - Al-Ahmad, Shadi
N1 - Funding Information:
This project is funded by National Council of Thailand (NRCT) under Research Grants for Talented Mid-Career Researchers (Contract no. N41A640089).
Funding Information:
The authors acknowledge the financial support provided by the Center of Excellence in Theoretical and Computational Science (TaCS-CoE), KMUTT and Center under Computational and Applied Science for Smart Innovation research Cluster (CLASSIC), Faculty of Science, KMUTT. Aliyu Muhammed Awwal would like to thank the Postdoctoral Fellowship from King Mongkut’s University of Technology Thonburi (KMUTT), Thailand.
Publisher Copyright:
© 2021, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot.
AB - The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot.
KW - Coronavirus (COVID-19)
KW - Finite difference
KW - Line search procedure
KW - Motion control
KW - Optimization models
KW - Regression analysis
KW - Three-term CG algorithms
UR - http://www.scopus.com/inward/record.url?scp=85122333551&partnerID=8YFLogxK
U2 - 10.1186/s13662-021-03638-9
DO - 10.1186/s13662-021-03638-9
M3 - Article
AN - SCOPUS:85122333551
VL - 2022
JO - Advances in Continuous and Discrete Models
JF - Advances in Continuous and Discrete Models
SN - 2731-4235
IS - 1
M1 - 1
ER -