Open AccessSolving a large scale nonlinear unconstrained optimization problems by using new coefficient of conjugate gradient method with exact line search direction
Author(s) -
Talat Alkouli,
Mustafa Mamat,
Mohd Rivaie,
Puspa Liza Ghazali
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.28.20976
Subject(s) - conjugate gradient method , nonlinear conjugate gradient method , line search , gradient descent , convergence (economics) , conjugate residual method , gradient method , derivation of the conjugate gradient method , mathematical optimization , scale (ratio) , descent (aeronautics) , nonlinear system , line (geometry) , mathematics , descent direction , set (abstract data type) , conjugate , computer science , algorithm , artificial intelligence , artificial neural network , mathematical analysis , physics , geometry , computer security , quantum mechanics , aerospace engineering , economics , engineering , radius , programming language , economic growth
In this paper, an efficient modification of nonlinear conjugate gradient method and an associated implementation, based on an exact line search, are proposed and analyzed to solve large-scale unconstrained optimization problems. The method satisfies the sufficient descent property. Furthermore, global convergence result is proved. Computational results for a set of unconstrained optimization test problems, some of them from CUTE library, showed that this new conjugate gradient algorithm seems to converge more stable and outperforms the other similar methods in many situations.