Open AccessPerformance Analysis of a Modified Conjugate Gradient Algorithm for Optimization Models
Author(s) -
S. E. Olowo,
Ibrahim Mohammed Sulaiman,
Mustafa Mamat,
Abiodun Ezekiel Owoyemi,
M. Arul Zaini,
Kalfin Kalfin,
Siti Hadiaty Yuningsih
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1115/1/012004
Subject(s) - conjugate gradient method , nonlinear conjugate gradient method , convergence (economics) , conjugate residual method , computation , line search , algorithm , gradient method , derivation of the conjugate gradient method , minification , mathematical optimization , computer science , conjugate , mathematics , gradient descent , artificial intelligence , artificial neural network , mathematical analysis , computer security , radius , economics , economic growth
The Conjugate gradient (CG) algorithms is very important and widely used in solving optimization models. This is due to its simplicity as well as global convergence properties. Various line search procedures as usually employ in the analysis of the CG methods. Recently, many studies have been done aimed at improving the CG method. In this paper, an alternative formula for conjugate gradient coefficient has been proposed which possesses the global convergence properties under exact minimization condition. The result of the numerical computation has shown that this new coefficient performs better than the existing CG methods.