A modified Hestenes-Stiefel method for solving unconstrained optimization problems
Abstract
The conjugate gradient methods are among the most efficient methods for solving optimization models. This is due to its simplicity, low memory requirement and the properties of its global convergent. Many researchers try to improve this technique. In this paper, we suggested a modification of the conjugate gradient parameter with global convergence properties via exact minimization rule. Preliminary experiment was conducted using some unconstrained optimization benchmark problems. Numerical outcome showed that the new algorithm is efficient and promising as it performs better than other classical methods both in terms of number of iteration and CPU time.
Copyright © 2020 Alkhouli Talat, Ibrahim Mohammed Sulaiman, Mustafa Mamat, Mohd Rivaie. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article
A. Talat, I. M. Sulaiman, M. Mamat, M. Rivaie, A modified Hestenes-Stiefel method for solving unconstrained optimization problems, J. Math. Comput. Sci. 10 (2020), 2126–2138. https://doi.org/10.28919/jmcs/4844
BibTeX
@article{Talat2020,
author = {Talat, Alkhouli and Sulaiman, Ibrahim Mohammed and Mamat, Mustafa and Rivaie, Mohd},
title = {A modified Hestenes-Stiefel method for solving unconstrained optimization problems},
journal = {J. Math. Comput. Sci.},
volume = {10},
year = {2020},
pages = {2126--2138},
doi = {10.28919/jmcs/4844}
}