JMSS Cover
Frequency: Continuous
ISSN: 1549-3644 (Print)
ISSN: 1558-6359 (Online)
Research Article Open Access

A Robust Modification of Hestenes–Stiefel Conjugate Gradient Method with Strong Wolfe Line Search

Awad Abdelrahman1, Osman O.O. Yousif1 and Khalid I. Osman2
  • 1 University of Gezira, Sudan
  • 2 King Khalid University, Saudi Arabia

Abstract

Nonlinear Conjugate Gradient (CG) methods are extensively used for solving large-scale unconstrained optimization problems. Numerous of studies constructed scales and modifications have been conducted recently to improve (CG) methods. In this paper, a simple modified by its conjugate gradient method was proposed. In addition to, established global convergence property and sufficient descent condition, under Strong Wolfe line search. Numerical result shows that the proposed formula is competitive when compared to other well-known (CG) parameters.

Journal of Mathematics and Statistics
Volume 16 No. 1, 2020, 54-61

DOI: https://doi.org/10.3844/jmssp.2020.54.61

Submitted On: 9 February 2020 Published On: 14 May 2020

How to Cite: Abdelrahman, A., Yousif, O. O. & Osman, K. I. (2020). A Robust Modification of Hestenes–Stiefel Conjugate Gradient Method with Strong Wolfe Line Search. Journal of Mathematics and Statistics, 16(1), 54-61. https://doi.org/10.3844/jmssp.2020.54.61

Open AccessCopyright: © 2020 Awad Abdelrahman, Osman O.O. Yousif and Khalid I. Osman. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

  • 3,313 Views
  • 1,373 Downloads
  • 0 Citations

Download

Keywords

  • Unconstrained Optimization
  • Conjugate Gradient Method
  • Sufficient Descent Property
  • Global Convergence