共轭梯度法
数学
期限(时间)
非线性共轭梯度法
下降方向
行搜索
共轭梯度法的推导
梯度下降
下降(航空)
梯度法
共轭残差法
应用数学
数学优化
近端梯度法
算法
计算机科学
几何学
凸函数
正多边形
人工神经网络
物理
计算机安全
量子力学
工程类
半径
航空航天工程
机器学习
作者
J.K. Liu,Yongxiang Zhao,Xiaoyu Wu
标识
DOI:10.1016/j.apnum.2019.10.011
摘要
In this paper, a three-term conjugate gradient method with the new direction structure is proposed for solving large-scale unconstrained optimization problems, which generates a sufficient descent direction in per-iteration by the aid of some inexact line search conditions. Under suitable assumptions, the proposed method is globally convergent for nonconvex smooth problems. We further generalize the new direction structure to other traditional methods and obtain some algorithms with the same structure as the proposed method. Preliminary numerical comparisons show that the proposed methods are effective and promising for the test problems.
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