Sabrina Ben Hanachi Sellami, Badreddine Sellami and Mohammed Belloufi (2023) A new family of hybrid conjugate gradient method for unconstrained optimization and its application to regression analysis. RAIRO - Operations Research , (),
Scientific Publications
Important: This page is frozen. New documents are now available in the digital repository DSpace
Abstract
We know many conjugate gradient algorithms (CG) for solving unconstrained optimization problems. In this paper, based on the three famous Liu-Storey (LS), Fletcher-Reeves (FR) and Polak-Ribiére-Polyak (PRP) conjugate gradient methods, a new hybrid CG method is proposed. Furthermore, the search direction satisfies the sufficient descent condition independent of the line search. Likewise, we prove, under the strong Wolfe line search, the global convergence of the new method. In this respect, numerical experiments are performed and reported, which show that the proposed method is efficient and promising. In virtue of this, the application of the proposed method for solving regression models of COVID-19 is provided.
Information
| Item Type | Journal |
|---|---|
| Divisions |
» Laboratory of Computer Science and Mathematics |
| ePrint ID | 4863 |
| Date Deposited | 2024-02-19 |
| Further Information | Google Scholar |
| URI | https://univ-soukahras.dz/en/publication/article/4863 |
BibTex
@article{uniusa4863,
title={A new family of hybrid conjugate gradient method for unconstrained optimization and its application to regression analysis},
author={Sabrina Ben Hanachi Sellami, Badreddine Sellami and Mohammed Belloufi},
journal={RAIRO - Operations Research}
year={2023},
volume={},
number={},
pages={},
publisher={}
}
title={A new family of hybrid conjugate gradient method for unconstrained optimization and its application to regression analysis},
author={Sabrina Ben Hanachi Sellami, Badreddine Sellami and Mohammed Belloufi},
journal={RAIRO - Operations Research}
year={2023},
volume={},
number={},
pages={},
publisher={}
}