scholarly journals Global convergence of a modified LS nonlinear conjugate gradient method

2011 ◽  
Vol 15 ◽  
pp. 4357-4361
Author(s):  
Jinkui Liu ◽  
Yuming Feng
2012 ◽  
Vol 2012 ◽  
pp. 1-14
Author(s):  
Yang Yueting ◽  
Cao Mingyuan

We propose and generalize a new nonlinear conjugate gradient method for unconstrained optimization. The global convergence is proved with the Wolfe line search. Numerical experiments are reported which support the theoretical analyses and show the presented methods outperforming CGDESCENT method.


2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Tiefeng Zhu ◽  
Zaizai Yan ◽  
Xiuyun Peng

A general criterion for the global convergence of the nonlinear conjugate gradient method is established, based on which the global convergence of a new modified three-parameter nonlinear conjugate gradient method is proved under some mild conditions. A large amount of numerical experiments is executed and reported, which show that the proposed method is competitive and alternative. Finally, one engineering example has been analyzed for illustrative purposes.


Author(s):  
Chergui Ahmed ◽  
Bouali Tahar

In this paper, We propose a new nonlinear conjugate gradient method (FRA) that satisfies a sufficient descent condition and global convergence under the inexact line search of strong wolf powell. Our numerical experiment shaw the efficiency of the new method in solving a set of problems from the CUTEst package, the proposed new formula gives excellent numerical results at CPU time, number of iterations, number of gradient ratings when compared to WYL, DY, PRP, and FR methods.


2011 ◽  
Vol 2011 ◽  
pp. 1-22
Author(s):  
Liu Jin-kui ◽  
Zou Li-min ◽  
Song Xiao-qian

A modified PRP nonlinear conjugate gradient method to solve unconstrained optimization problems is proposed. The important property of the proposed method is that the sufficient descent property is guaranteed independent of any line search. By the use of the Wolfe line search, the global convergence of the proposed method is established for nonconvex minimization. Numerical results show that the proposed method is effective and promising by comparing with the VPRP, CG-DESCENT, and DL+methods.


Sign in / Sign up

Export Citation Format

Share Document