scholarly journals Modified new conjugate gradient method for Unconstrained Optimization

2019 ◽  
Vol 24 (5) ◽  
pp. 86
Author(s):  
Zeyad M. Abdullah1 ◽  
Hameed M, Sadeq2 ◽  
, Hisham M, Azzam3 ◽  
Mundher A. Khaleel4
Keyword(s):  
Nonlinear Optimization ◽  
Unconstrained Optimization ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Current Paper ◽  
Modified Method ◽  
Unconstrained Nonlinear Optimization ◽  
Statistical Results

The current paper modified method of conjugate gradient for solving problems of unconstrained optimization. The modified method convergence is achieved by assuming some hypotheses. The statistical results demonstrate that the modified method is efficient for solving problems of Unconstrained Nonlinear Optimization in comparison with methods FR and HS.   http://dx.doi.org/10.25130/tjps.24.2019.095

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.

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