Open AccessGlobal convergence of new conjugate gradient method with inexact line search
Author(s) -
Chergui Ahmed,
Tahar Bouali
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v11i2.pp1469-1475
Subject(s) - conjugate gradient method , nonlinear conjugate gradient method , line search , convergence (economics) , gradient descent , conjugate residual method , set (abstract data type) , line (geometry) , descent (aeronautics) , derivation of the conjugate gradient method , conjugate , gradient method , mathematics , descent direction , computer science , mathematical optimization , algorithm , mathematical analysis , geometry , artificial intelligence , physics , computer security , meteorology , artificial neural network , economics , radius , programming language , economic growth
In this paper, We propose a new nonlinear conjugate gradient method (FRA) that satisfies a sufficient descent condition and global convergence under the inexact line search of strong wolf powell. Our numerical experiment shaw the efficiency of the new method in solving a set of problems from the CUTEst package, the proposed new formula gives excellent numerical results at CPU time, number of iterations, number of gradient ratings when compared to WYL, DY, PRP, and FR methods.