A Geometric Nonlinear Conjugate Gradient Method for Stochastic Inverse Eigenvalue Problems

2016 ◽  
Vol 54 (4) ◽  
pp. 2015-2035 ◽  
Author(s):  
Zhi Zhao ◽  
Xiao-Qing Jin ◽  
Zheng-Jian Bai
Keyword(s):  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Eigenvalue Problems ◽  
Nonlinear Conjugate Gradient Method ◽  
Inverse Eigenvalue Problems ◽  
Geometric Nonlinear ◽  
Nonlinear Conjugate Gradient ◽  
Inverse Eigenvalue

scholarly journals A Modified Conjugacy Condition and Related Nonlinear Conjugate Gradient Method

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shengwei Yao ◽  
Xiwen Lu ◽  
Bin Qin
Keyword(s):  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Large Scale ◽  
Optimization Problems ◽  
Special Role ◽  
Conjugacy Condition ◽  
Nonlinear Conjugate Gradient Method ◽  
Nonlinear Conjugate Gradient ◽  
The Given

The conjugate gradient (CG) method has played a special role in solving large-scale nonlinear optimization problems due to the simplicity of their very low memory requirements. In this paper, we propose a new conjugacy condition which is similar to Dai-Liao (2001). Based on this condition, the related nonlinear conjugate gradient method is given. With some mild conditions, the given method is globally convergent under the strong Wolfe-Powell line search for general functions. The numerical experiments show that the proposed method is very robust and efficient.

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