Amina Hallal, Mohammed Belloufi and Sellami Badreddine (2023) A new hybrid CG method as convex combination. Mathematical Foundations of Computing , (),
Scientific Publications
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Abstract
Conjugate gradient methods are among the most efficient methods
for solving optimization models. In this paper, a newly proposed conjugate
gradient method is proposed for solving optimization problems as a convex
combination of the Harger-Zhan and Dai-Yaun nonlinear conjugate gradient
methods, which is capable of producing the sufficient descent condition with
global convergence properties under the strong Wolfe conditions. The numerical
results demonstrate the efficiency of the proposed method with some
benchmark problems.
for solving optimization models. In this paper, a newly proposed conjugate
gradient method is proposed for solving optimization problems as a convex
combination of the Harger-Zhan and Dai-Yaun nonlinear conjugate gradient
methods, which is capable of producing the sufficient descent condition with
global convergence properties under the strong Wolfe conditions. The numerical
results demonstrate the efficiency of the proposed method with some
benchmark problems.
Information
| Item Type | Journal |
|---|---|
| Divisions |
» Laboratory of Computer Science and Mathematics |
| ePrint ID | 4861 |
| Date Deposited | 2024-02-19 |
| Further Information | Google Scholar |
| URI | https://univ-soukahras.dz/en/publication/article/4861 |
BibTex
@article{uniusa4861,
title={A new hybrid CG method as convex combination},
author={Amina Hallal, Mohammed Belloufi and Sellami Badreddine},
journal={Mathematical Foundations of Computing}
year={2023},
volume={},
number={},
pages={},
publisher={}
}
title={A new hybrid CG method as convex combination},
author={Amina Hallal, Mohammed Belloufi and Sellami Badreddine},
journal={Mathematical Foundations of Computing}
year={2023},
volume={},
number={},
pages={},
publisher={}
}