.png){kind=icon}
.png){kind=icon}
Return
Optimizing the conjugate gradient algorithm and its diverse applications
.png){kind=icon}
PRE
AI
DOI：10.1080/00207160.2026.2637715.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The development of a new conjugate gradient method, derived from the modified difference gradient vector and based on an approximation of the Hessian matrix, marks a significant advancement in optimization algorithms. This study shows that the robustness of the proposed method, particularly in applications such as compressive sensing and image processing. Its global convergence properties and sufficient descent conditions ensure reliable performance even in computationally challenging scenarios. Extensive numerical experiments in signal recovery, image restoration, and unconstrained optimization clearly illustrate the superiority of the proposed method over existing approaches, including traditional conjugate gradient techniques. The proposed CG method not only boosts computational efficiency but also significantly enhances the quality of solutions for both signal recovery and image restoration. These promising results emphasize the method's potential as a versatile and powerful tool for solving a wide range of practical problems within computational mathematics, optimization, and signal processing.
Keywords:
Optimization algorithms
conjugate gradient
compressive sensing
signal recovery
image restoration
Journal
I
IF:
1.3
Papers:
83
Citations:
0