A New Scalar of Conjugate Gradient Methods for Solving Unconstrained Minimization

Authors

  • Saja O. Mohammad T. Department of Mathematics, College of Basic Education, University of Mosul, Mosul, Iraq.
  • Hamsa Th. Saeed Chilmeran Department of Mathematics, College of Computer Sciences and Mathematics, University of Mosul, Iraq
  • Rana Z. Al-Kawaz University of Telafer / College of Basic Education https://orcid.org/0000-0002-3087-4310

DOI:

https://doi.org/10.29020/nybg.ejpam.v16i1.4619

Keywords:

conjugate-gradient, self-scaling, Quasi Newton-method, sufficient descent, global convergence

Abstract

In this paper, we derive a search direction for the conjugate-gradient method based on use of self-scaling Quasi Newton-method, and the usefulness of the new method is to solve unconstrained optimization problems with large dimensions. In order to clarify the importance of the proposed method, we have shown its characteristics in terms of the sufficient descent condition and the theoretically global convergence condition. Numerically, we applied the proposed method to a variety of known test functions to prove its effectiveness. When compared with some previous methods of the same direction, the proposed method proved to be superior to them in relation to the tools used for this purpose.

Author Biographies

  • Saja O. Mohammad T., Department of Mathematics, College of Basic Education, University of Mosul, Mosul, Iraq.

    Department of Mathematics, College of Basic Education, University of Mosul, Mosul, Iraq.

  • Hamsa Th. Saeed Chilmeran, Department of Mathematics, College of Computer Sciences and Mathematics, University of Mosul, Iraq

    Department of Mathematics, College of Computer Sciences and Mathematics, University of Mosul, Iraq.

  • Rana Z. Al-Kawaz, University of Telafer / College of Basic Education

    Department of Mathematics, College of Basic Education, University of Telafer, Mosul, Iraq.

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Published

2023-01-29

Issue

Section

Nonlinear Analysis

How to Cite

A New Scalar of Conjugate Gradient Methods for Solving Unconstrained Minimization. (2023). European Journal of Pure and Applied Mathematics, 16(1), 233-242. https://doi.org/10.29020/nybg.ejpam.v16i1.4619

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