Modified spectral conjugate gradient iterative scheme for unconstrained optimization problems with application on COVID-19 model

Fevi Novkaniza; Maulana Malik; Ibrahim Mohammed Sulaiman; Dipo Aldila

doi:10.3389/fams.2022.1014956

Modified spectral conjugate gradient iterative scheme for unconstrained optimization problems with application on COVID-19 model

Fevi Novkaniza, Maulana Malik, Ibrahim Mohammed Sulaiman, Dipo Aldila

Department of Mathematics

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

In this work, a new class of spectral conjugate gradient (CG) method is proposed for solving unconstrained optimization models. The search direction of the new method uses the ZPRP and JYJLL CG coefficients. The search direction satisfies the descent condition independent of the line search. The global convergence properties of the proposed method under the strong Wolfe line search are proved with some certain assumptions. Based on some test functions, numerical experiments are presented to show the proposed method's efficiency compared with other existing methods. The application of the proposed method for solving regression models of COVID-19 is provided. Mathematics subject classification: 65K10, 90C52, 90C26.

Original language	English
Article number	1014956
Journal	Frontiers in Applied Mathematics and Statistics
Volume	8
DOIs	https://doi.org/10.3389/fams.2022.1014956
Publication status	Published - 8 Nov 2022

Keywords

descent condition
global convergence
regression models
spectral conjugate gradient method
unconstrained optimization

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10.3389/fams.2022.1014956

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title = "Modified spectral conjugate gradient iterative scheme for unconstrained optimization problems with application on COVID-19 model",

abstract = "In this work, a new class of spectral conjugate gradient (CG) method is proposed for solving unconstrained optimization models. The search direction of the new method uses the ZPRP and JYJLL CG coefficients. The search direction satisfies the descent condition independent of the line search. The global convergence properties of the proposed method under the strong Wolfe line search are proved with some certain assumptions. Based on some test functions, numerical experiments are presented to show the proposed method's efficiency compared with other existing methods. The application of the proposed method for solving regression models of COVID-19 is provided. Mathematics subject classification: 65K10, 90C52, 90C26.",

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author = "Fevi Novkaniza and Maulana Malik and Sulaiman, {Ibrahim Mohammed} and Dipo Aldila",

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AU - Malik, Maulana

AU - Sulaiman, Ibrahim Mohammed

AU - Aldila, Dipo

PY - 2022/11/8

Y1 - 2022/11/8

N2 - In this work, a new class of spectral conjugate gradient (CG) method is proposed for solving unconstrained optimization models. The search direction of the new method uses the ZPRP and JYJLL CG coefficients. The search direction satisfies the descent condition independent of the line search. The global convergence properties of the proposed method under the strong Wolfe line search are proved with some certain assumptions. Based on some test functions, numerical experiments are presented to show the proposed method's efficiency compared with other existing methods. The application of the proposed method for solving regression models of COVID-19 is provided. Mathematics subject classification: 65K10, 90C52, 90C26.

AB - In this work, a new class of spectral conjugate gradient (CG) method is proposed for solving unconstrained optimization models. The search direction of the new method uses the ZPRP and JYJLL CG coefficients. The search direction satisfies the descent condition independent of the line search. The global convergence properties of the proposed method under the strong Wolfe line search are proved with some certain assumptions. Based on some test functions, numerical experiments are presented to show the proposed method's efficiency compared with other existing methods. The application of the proposed method for solving regression models of COVID-19 is provided. Mathematics subject classification: 65K10, 90C52, 90C26.

KW - descent condition

KW - global convergence

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KW - spectral conjugate gradient method

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JO - Frontiers in Applied Mathematics and Statistics

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Modified spectral conjugate gradient iterative scheme for unconstrained optimization problems with application on COVID-19 model

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