共轭梯度法
数学
梯度下降
行搜索
期限(时间)
非线性共轭梯度法
趋同(经济学)
共轭残差法
算法
下降(航空)
下降方向
梯度法
直线(几何图形)
共轭梯度法的推导
数学优化
结合
计算机科学
人工智能
几何学
人工神经网络
数学分析
计算机安全
航空航天工程
经济
半径
工程类
物理
经济增长
量子力学
作者
Xianzhen Jiang,Huihui Yang,Jianghua Yin,Wei Liao
标识
DOI:10.1016/j.cam.2022.115020
摘要
Based on the Liu–Storey conjugate gradient method, a three-term conjugate gradient method with restart procedure is proposed for unconstrained optimization. The designed restart condition is chosen according to the Liu–Storey conjugate parameter, and the restart direction consists of the first and third items in the non-restart direction, which can be picked flexibly. Without depending on any line search, the resulting direction satisfies the sufficient descent condition. Moreover, the proposed method possesses the global convergence under usual assumptions and the weak Wolfe line search. Finally, medium–large-scale numerical experiments are conducted for solving unconstrained optimization and image restoration problems, and the numerical results show that the proposed method is promising and practical, even compared with the state-of-the-art methods.
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