Sulaiman Ibrahim Mohammed, Malik Maulana, Awwal Aliyu Muhammed, Kumam Poom, Mamat Mustafa, Al-Ahmad Shadi
Department of Mathematics and Statistics, School of Quantitative Sciences, College of Art and Sciences (CAS), Universiti Utara Malaysia (UUM), 06010 Sintok, Kedah Malaysia.
Department of Mathematics, Universitas Indonesia (UI), Depok, 16424 Indonesia.
Adv Contin Discret Model. 2022;2022(1):1. doi: 10.1186/s13662-021-03638-9. Epub 2022 Jan 4.
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.
三项共轭梯度（CG）算法是求解优化模型的CG算法的高效变体之一。这得益于它们的简单性和低内存需求。另一方面，回归模型是一种统计关系模型，其解是使用包括类CG方法在内的最小二乘法之一获得的。在本文中，我们提出了一种用于无约束优化模型的三项共轭梯度法的改进，并进一步建立了不精确线搜索下的全局收敛性。所提出的方法被扩展用于构建新型冠状病毒（COVID-19）的回归模型。该研究考虑了2020年1月至10月的全球感染病例来对模型进行参数化。初步结果表明，所提出的方法很有前景，并为COVID-19大流行产生了有效的回归模型。此外，该方法被扩展用于解决涉及双关节平面机器人的运动控制问题。
