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

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.

Download Full-text

A new modified scaled conjugate gradient method for large-scale unconstrained optimization with non-convex objective function

Optimization Methods and Software ◽

10.1080/10556788.2018.1457152 ◽

2018 ◽

Vol 34 (4) ◽

pp. 783-796 ◽

Cited By ~ 2

Author(s):

Zahra Khoshgam ◽

Ali Ashrafi

Keyword(s):

Objective Function ◽

Unconstrained Optimization ◽

Conjugate Gradient Method ◽

Conjugate Gradient ◽

Gradient Method ◽

Large Scale ◽

Scaled Conjugate Gradient ◽

Convex Objective Function ◽

Large Scale Unconstrained Optimization

Download Full-text

Modified new conjugate gradient method for Unconstrained Optimization

Tikrit Journal of Pure Science ◽

10.25130/j.v24i5.872 ◽

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

Download Full-text

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

Abstract and Applied Analysis ◽

10.1155/2013/814912 ◽

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.

Download Full-text

A scaled conjugate gradient method with moving asymptotes for unconstrained optimization problems

Journal of Industrial & Management Optimization ◽

10.3934/jimo.2016034 ◽

2017 ◽

Vol 13 (2) ◽

pp. 595-608

Author(s):

Guanghui Zhou ◽

◽

Qin Ni ◽

Meilan Zeng ◽

◽

...

Keyword(s):

Unconstrained Optimization ◽

Conjugate Gradient Method ◽

Conjugate Gradient ◽

Gradient Method ◽

Optimization Problems ◽

Scaled Conjugate Gradient ◽

Unconstrained Optimization Problems ◽

Moving Asymptotes

Download Full-text

A New conjugate gradient method for unconstrained optimization problems with descent property

General Letters in Mathematics ◽

10.31559/glm2020.9.2.6 ◽

2020 ◽

Vol 9 (2) ◽

pp. 101-105

Author(s):

Hussein Ageel Khatab ◽

Salah Gazi Shareef

Keyword(s):

Unconstrained Optimization ◽

Conjugate Gradient Method ◽

Conjugate Gradient ◽

Gradient Method ◽

Optimization Problems ◽

Conjugacy Condition ◽

Sufficient Descent Condition ◽

Unconstrained Optimization Problems ◽

Descent Condition ◽

Nonlinear Unconstrained Optimization

In this paper, we propose a new conjugate gradient method for solving nonlinear unconstrained optimization. The new method consists of three parts, the first part of them is the parameter of Hestenes-Stiefel (HS). The proposed method is satisfying the descent condition, sufficient descent condition and conjugacy condition. We give some numerical results to show the efficiency of the suggested method.

Download Full-text

A new spectral conjugate gradient method with descent condition and global convergence property for unconstrained optimization

Journal of Mathematical and Computational Science ◽

10.28919/jmcs/4829 ◽

2020 ◽

Keyword(s):

Global Convergence ◽

Unconstrained Optimization ◽

Conjugate Gradient Method ◽

Conjugate Gradient ◽

Gradient Method ◽

Convergence Property ◽

Global Convergence Property ◽

Descent Condition ◽

Spectral Conjugate Gradient

Download Full-text

A New Conjugate Gradient Coefficient for Unconstrained Optimization Based On Dai-Liao

Science Journal of University of Zakho ◽

10.25271/sjuoz.2019.7.1.525 ◽

2019 ◽

Vol 7 (1) ◽

pp. 34-36

Author(s):

Alaa L. Ibrahim ◽

Muhammad A. Sadiq ◽

Salah G. Shareef

Keyword(s):

Unconstrained Optimization ◽

Conjugate Gradient Method ◽

Conjugate Gradient ◽

Gradient Method ◽

Numerical Results ◽

Sufficient Descent Condition ◽

Sufficient Descent ◽

Descent Condition ◽

Number Of Iterations

This paper, proposes a new conjugate gradient method for unconstrained optimization based on Dai-Liao (DL) formula; descent condition and sufficient descent condition for our method are provided. The numerical results and comparison show that the proposed algorithm is potentially efficient when we compare with (PR) depending on number of iterations (NOI) and the number of functions evaluation (NOF).

Download Full-text