Open AccessA New Family of Hybrid Conjugate Gradient Methods for Unconstrained Optimization
Author(s) -
O. J. Adeleke,
Absalom E. Ezugwu,
Idowu Ademola Osinuga
Publication year - 2020
Publication title -
statistics, optimization and information computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.297
H-Index - 12
eISSN - 2311-004X
pISSN - 2310-5070
DOI - 10.19139/soic-2310-5070-480
Subject(s) - conjugate gradient method , line search , derivation of the conjugate gradient method , conjugate residual method , gradient method , gradient descent , computer science , mathematical optimization , nonlinear conjugate gradient method , mathematics , profiling (computer programming) , algorithm , artificial intelligence , artificial neural network , computer security , radius , operating system
The conjugate gradient method is a very efficient iterative technique for solving large-scale unconstrainedoptimization problems. Motivated by recent modifications of some variants of the method and construction of hybrid methods, this study proposed four hybrid methods that are globally convergent as well as computationally efficient. The approach adopted for constructing the hybrid methods entails projecting ten recently modified conjugate gradient methods. Each of the hybrid methods is shown to satisfy the descent property independent of any line search technique and globally convergent under the influence of strong Wolfe line search. Results obtained from numerical implementation of these methods and performance profiling show that the methods are very competitive with well-known traditional methods.