| Issue |
RAIRO-Oper. Res.
Volume 57, Number 2, March-April 2023
|
|
|---|---|---|
| Page(s) | 817 - 835 | |
| DOI | https://doi.org/10.1051/ro/2023037 | |
| Published online | 28 April 2023 | |
A modified nonlinear conjugate gradient algorithm for unconstrained optimization and portfolio selection problems
Department of Mathematics, University of Botswana, Private Bag UB00704, Gaborone, Botswana
* Corresponding author: kaelop@ub.ac.bw
Received:
5
June
2022
Accepted:
25
March
2023
Conjugate gradient methods play a vital role in finding solutions of large-scale optimization problems due to their simplicity to implement, low memory requirements and as well as their convergence properties. In this paper, we propose a new conjugate gradient method that has a direction satisfying the sufficient descent property. We establish global convergence of the new method under the strong Wolfe line search conditions. Numerical results show that the new method performs better than other relevant methods in the literature. Furthermore, we use the new method to solve a portfolio selection problem.
Mathematics Subject Classification: 90C06 / 90C30 / 65K05
Key words: Conjugate gradient / global convergence / strong Wolfe line search / portfolio selection
© The authors. Published by EDP Sciences, ROADEF, SMAI 2023
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