A New Family of Hybrid Conjugate Gradient Methods for Unconstrained Optimization

Autor:	Idowu Ademola Osinuga, Absalom E. Ezugwu, Olawale J. Adeleke
Rok vydání:	2020
Předmět:	Statistics and Probability Control and Optimization Artificial Intelligence Conjugate gradient method Signal Processing Applied mathematics Computer Vision and Pattern Recognition Unconstrained optimization Statistics Probability and Uncertainty Information Systems Mathematics
Zdroj:	Statistics, Optimization & Information Computing. 9:399-417
ISSN:	2310-5070 2311-004X
DOI:	10.19139/soic-2310-5070-480
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5232aaddbb799661ac6d5857e32d61fa https://doi.org/10.19139/soic-2310-5070-480 Zobrazit plný text záznamu