An efficient modified HS conjugate gradient algorithm in machine learning
The Hestenes-Stiefe (HS) conjugate gradient method is very effective in resolving larger-scale sophisticated smoothing optimization tasks due to its low computational requirements and high computational efficiency. Additionally, the algorithm has been employed in practical applications to address im...
Saved in:
Main Authors: | Gonglin Yuan, Minjie Huang |
---|---|
Format: | Article |
Language: | English |
Published: |
AIMS Press
2024-11-01
|
Series: | Electronic Research Archive |
Subjects: | |
Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2024287 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Global convergence in a modified RMIL-type conjugate gradient algorithm for nonlinear systems of equations and signal recovery
by: Yan Xia, et al.
Published: (2024-11-01) -
A hybrid approach to conjugate gradient algorithms for nonlinear systems of equations with applications in signal restoration
by: Xuejie Ma, et al.
Published: (2024-12-01) -
Short Paper - Quadratic minimization: from conjugate gradient to an adaptive Polyak’s momentum method with Polyak step-sizes
by: Goujaud, Baptiste, et al.
Published: (2024-11-01) -
Numerical optimization of large-scale monotone equations using the free-derivative spectral conjugate gradient method
by: Ghulam Abbass, et al.
Published: (2025-01-01) -
Dynamic Conjugate Gradient Unfolding for Symbol Detection in Time-Varying Massive MIMO
by: Toluwaleke Olutayo, et al.
Published: (2024-01-01)