On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control

Ibrahim Mohammed Sulaiman, Maulana Malik, Aliyu Muhammed Awwal, Poom Kumam, Mustafa Mamat, Shadi Al-Ahmad

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number1
JournalAdvances in Continuous and Discrete Models
Volume2022
Issue number1
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Coronavirus (COVID-19)
  • Finite difference
  • Line search procedure
  • Motion control
  • Optimization models
  • Regression analysis
  • Three-term CG algorithms

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