A Hybrid Conjugate Gradient Method with Trust Region for Large-Scale Unconstrained Optimization Problems
Year: 2025
Author: A. P. Byengonzi, P. Kaelo, M. Koorapetse, P. Mtagulwa
Annals of Applied Mathematics, Vol. 41 (2025), Iss. 2 : pp. 176–193
Abstract
In this work, we modify a conjugate gradient (CG) method recently proposed in the literature, where a PRP conjugate gradient method is modified using trust region. Particularly, we propose a hybrid CG method that incorporates the parameters $β^{PRP},$ $β^{FR}$ and $β^{CD},$ and this new search direction satisfies both the trust region feature and the sufficient descent conditions. Furthermore, under suitable conditions the developed method is proved to be globally convergent. The method is tested on some benchmark problems from the literature and numerical results show that it is quite efficient in solving large scale problems.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/aam.OA-2024-0019
Annals of Applied Mathematics, Vol. 41 (2025), Iss. 2 : pp. 176–193
Published online: 2025-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 18
Keywords: Conjugate gradient method global convergence strong Wolfe line search trust-region.