Issue |
RAIRO-Oper. Res.
Volume 59, Number 1, January-February 2025
|
|
---|---|---|
Page(s) | 311 - 324 | |
DOI | https://doi.org/10.1051/ro/2024224 | |
Published online | 21 January 2025 |
A modified conjugate gradient method for unconstrained optimization with application in regression function
Laboratory Informatics and Mathematics (LIM), Mohamed Cherif Messaadia University, Souk Ahras 41000, Algeria
* Corresponding author: i.guefassa@univ-soukahras.dz
Received:
4
May
2023
Accepted:
6
December
2024
Due to its rapid convergence rate and low memory requirements, the conjugate gradient (CG) scheme is a popular technique for solving unconstrained optimization problems. This paper introduces a modified conjugate gradient method, GICY. By utilizing the strong Wolfe line search (SWLS), we prove that GICY satisfies the sufficient descent condition at every iteration, guaranteeing significant progress towards the minimum. Additionally, we establish the global convergence of the proposed method. Extensive numerical computations demonstrate the success and promise of this new algorithm, particularly when applied to practical problems involving nonparametric regression functions.
Mathematics Subject Classification: 90C30 / 65K05 / 62G05
Key words: Unconstrainted optimization / conjugate gradient method / regression function / kernel estimator
© The authors. Published by EDP Sciences, ROADEF, SMAI 2025
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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