scholarly journals A Dai–Yuan-type Riemannian conjugate gradient method with the weak Wolfe conditions

2015 ◽  
Vol 64 (1) ◽  
pp. 101-118 ◽  
Author(s):  
Hiroyuki Sato
Keyword(s):  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Wolfe Conditions

scholarly journals New Investigation for the Liu-Story Scaled Conjugate Gradient Method for Nonlinear Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Eman T. Hamed ◽  
Rana Z. Al-Kawaz ◽  
Abbas Y. Al-Bayati
Keyword(s):  
Nonlinear Optimization ◽  
Unconstrained Optimization ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Numerical Experiments ◽  
Scalar Parameter ◽  
Scaled Conjugate Gradient ◽  
Wolfe Conditions ◽  
Descent Condition

This article considers modified formulas for the standard conjugate gradient (CG) technique that is planned by Li and Fukushima. A new scalar parameter θkNew for this CG technique of unconstrained optimization is planned. The descent condition and global convergent property are established below using strong Wolfe conditions. Our numerical experiments show that the new proposed algorithms are more stable and economic as compared to some well-known standard CG methods.

scholarly journals A Self-Adjusting Spectral Conjugate Gradient Method for Large-Scale Unconstrained Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Yuanying Qiu ◽  
Dandan Cui ◽  
Wei Xue ◽  
Gaohang Yu
Keyword(s):  
Unconstrained Optimization ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Large Scale ◽  
Convergence Result ◽  
Large Scale Problems ◽  
Wolfe Conditions ◽  
Spectral Conjugate Gradient ◽  
Large Scale Unconstrained Optimization

This paper presents a hybrid spectral conjugate gradient method for large-scale unconstrained optimization, which possesses a self-adjusting property. Under the standard Wolfe conditions, its global convergence result is established. Preliminary numerical results are reported on a set of large-scale problems in CUTEr to show the convergence and efficiency of the proposed method.

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