Open AccessA New Conjugate Gradient Method with Exact Line Search
Author(s) -
Mouiyad Bani Yousef,
Mustafa Mamat,
Mohd Rivaie
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.33.28167
Subject(s) - conjugate gradient method , nonlinear conjugate gradient method , line search , conjugate residual method , derivation of the conjugate gradient method , convergence (economics) , gradient method , conjugate , biconjugate gradient method , line (geometry) , mathematical optimization , mathematics , scale (ratio) , computer science , gradient descent , mathematical analysis , geometry , physics , artificial intelligence , computer security , quantum mechanics , artificial neural network , economics , radius , economic growth
The nonlinear conjugate gradient (CG) method is a widely used approach for solving large-scale optimization problems in many fields, such as physics, engineering, economics, and design. The efficiency of this method is mainly attributable to its global convergence properties and low memory requirement. In this paper, a new conjugate gradient coefficient is proposed based on the Aini-Rivaie-Mustafa (ARM) method. Furthermore, the proposed method is proved globally convergent under exact line search. This is supported by the results of the numerical tests. The numerical performance of the new CG method better than other related and more efficient compared with previous CG methods.