应用数学2025，Vol.38Issue(2)：492-500,9.
凸约束非线性方程组的改进谱共轭梯度解法及图像去噪应用
Improved Spectral Conjugate Gradient Algorithm for Convex Constrained Nonlinear Equations with Its Application in Image Denoising
摘要
Abstract
To effectively solve large-scale nonlinear equations with convex constraints and image denoising problems,this paper introduced the spectral parameter to design a new hybrid search direction formula based on Dai-Yuan(DY)and Hestenes-Stiefel(HS)conjugate gradient methods,and proposed a hybrid spectral conjugate gradient projection algorithm with efficient line search and projection techniques.The search direction of the new algorithm automatically satisfies sufficient descent property and trust region feature without relying on any line search approaches.Under reasonable assumptions,the new algorithm possesses global convergence properties.Numerical results demonstrate that the new algorithm is more efficient and effective in solving image denoising problem when compared to existing similar algorithms.关键词
凸约束非线性方程组/大规模/谱共轭梯度法/全局收敛性/图像去噪Key words
Convex constrained nonlinear equations/Large-scale/Spectral conjugate gradient method/Global convergence/Image denoising分类
数理科学引用本文复制引用
李丹丹,吴加其,黎勇,王松华..凸约束非线性方程组的改进谱共轭梯度解法及图像去噪应用[J].应用数学,2025,38(2):492-500,9.基金项目
国家自然科学基金(11661009) （11661009）
广西自然科学基金(2024GXNSFAA010478,2020GXNS-FAA159069) （2024GXNSFAA010478,2020GXNS-FAA159069）
广州华商学院科研团队项目(2021HSKT01) （2021HSKT01）
