Open AccessThe application of new conjugate gradient methods in estimating data
Author(s) -
Syazni Shoid,
Norrlaili Shapiee,
Norhaslinda Zull,
Nur Hamizah Abdul Ghani,
Nur Syarafina Mohamed,
Mohd Rivaie,
Mustafa Mamat
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.14.11147
Subject(s) - conjugate gradient method , computer science , scale (ratio) , nonlinear conjugate gradient method , line search , mathematical optimization , algorithm , mathematics , gradient descent , artificial intelligence , physics , artificial neural network , computer security , quantum mechanics , radius
Many researchers are intended to improve the conjugate gradient (CG) methods as well as their applications in real life. Besides, CG become more interesting and useful in many disciplines and has important role for solving large-scale optimization problems. In this paper, three types of new CG coefficients are presented with application in estimating data. Numerical experiments show that the proposed methods have succeeded in solving problems under strong Wolfe Powell line search conditions.