| Issue |
RAIRO-Oper. Res.
Volume 56, Number 6, November-December 2022
|
|
|---|---|---|
| Page(s) | 4047 - 4056 | |
| DOI | https://doi.org/10.1051/ro/2022200 | |
| Published online | 29 November 2022 | |
An efficient new hybrid CG-method as convex combination of DY and CD and HS algorithms
Department of mathematics and informatics, Laboratory Informatics and Mathematics (LiM), Mohamed Cherif Messaadia University, Souk Ahras, Algeria
* Corresponding author: a.hallal@univ-soukahras.dz
Received:
1
June
2022
Accepted:
11
November
2022
In this paper, we proposed a new hybrid conjugate gradient algorithm for solving unconstrained optimization problems as a convex combination of the Dai-Yuan algorithm, conjugate-descent algorithm, and Hestenes-Stiefel algorithm. This new algorithm is globally convergent and satisfies the sufficient descent condition by using the strong Wolfe conditions. The numerical results show that the proposed nonlinear hybrid conjugate gradient algorithm is efficient and robust.
Mathematics Subject Classification: 49M37 / 65K05 / 90C06
Key words: Unconstrained optimization problem / hybrid conjugate gradient method / strong Wolfe line search / sufficient descent condition
© The authors. Published by EDP Sciences, ROADEF, SMAI 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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