Open AccessNonlinear Conjugate Gradient Method with Modified Armijo Condition to Solve Unconstrained Optimization
Author(s) -
H. A. Wasi,
Mushtak A. K. Shiker
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1818/1/012021
Subject(s) - conjugate gradient method , nonlinear conjugate gradient method , derivation of the conjugate gradient method , gradient method , conjugate residual method , mathematics , nonlinear system , conjugate , simple (philosophy) , mathematical optimization , gradient descent , computer science , mathematical analysis , physics , artificial neural network , philosophy , quantum mechanics , epistemology , machine learning
Conjugate gradient methods more used in the field of unconstrained optimization, particularly large scale problems, Armijo condition one of the simple rule are commonly used to analyses and applications of CG methods. In this paper we exhibit a new modified for the Armijo condition with established converges globally of the conjugate gradient method and best numerical results.