Global convergence analysis of a new hybrid conjugate gradient method for unconstrained optimization problems
In this paper, we propose a new hybrid conjugate gradient method for unconstrained optimization problems. The proposed method comprises of beta (DY), beta (WHY), beta (RAMI) and beta (New). The beta (New) was constructed purposely for this proposed hybrid method.The method possesses sufficient des...
Saved in:
Main Authors: | , |
---|---|
Format: | Article |
Published: |
Penerbit UTM Press
2017
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/80916/ http://dx.doi.org/10.11113/mjfas.v13n2.540 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.80916 |
---|---|
record_format |
eprints |
spelling |
my.utm.809162019-07-24T00:10:41Z http://eprints.utm.my/id/eprint/80916/ Global convergence analysis of a new hybrid conjugate gradient method for unconstrained optimization problems Abdullahi, I. Ahmad, R. Q Science (General) In this paper, we propose a new hybrid conjugate gradient method for unconstrained optimization problems. The proposed method comprises of beta (DY), beta (WHY), beta (RAMI) and beta (New). The beta (New) was constructed purposely for this proposed hybrid method.The method possesses sufficient descent property irrespective of the line search. Under Strong Wolfe-Powell line search, we proved that the method is globally convergent. Numerical experimentation shows the effectiveness and robustness of the proposed method when compare with some hybrid as well as some modified conjugate gradient methods. Penerbit UTM Press 2017 Article PeerReviewed Abdullahi, I. and Ahmad, R. (2017) Global convergence analysis of a new hybrid conjugate gradient method for unconstrained optimization problems. Malaysian Journal of Fundamental and Applied Sciences, 13 (2). ISSN 2289-5981 http://dx.doi.org/10.11113/mjfas.v13n2.540 DOI:10.11113/mjfas.v13n2.540 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
topic |
Q Science (General) |
spellingShingle |
Q Science (General) Abdullahi, I. Ahmad, R. Global convergence analysis of a new hybrid conjugate gradient method for unconstrained optimization problems |
description |
In this paper, we propose a new hybrid conjugate gradient method for unconstrained optimization problems. The proposed method comprises of beta (DY), beta (WHY), beta (RAMI) and beta (New). The beta (New) was constructed purposely for this proposed hybrid method.The method possesses sufficient descent property irrespective of the line search. Under Strong Wolfe-Powell line search, we proved that the method is globally convergent. Numerical experimentation shows the effectiveness and robustness of the proposed method when compare with some hybrid as well as some modified conjugate gradient methods. |
format |
Article |
author |
Abdullahi, I. Ahmad, R. |
author_facet |
Abdullahi, I. Ahmad, R. |
author_sort |
Abdullahi, I. |
title |
Global convergence analysis of a new hybrid conjugate gradient method for unconstrained optimization problems |
title_short |
Global convergence analysis of a new hybrid conjugate gradient method for unconstrained optimization problems |
title_full |
Global convergence analysis of a new hybrid conjugate gradient method for unconstrained optimization problems |
title_fullStr |
Global convergence analysis of a new hybrid conjugate gradient method for unconstrained optimization problems |
title_full_unstemmed |
Global convergence analysis of a new hybrid conjugate gradient method for unconstrained optimization problems |
title_sort |
global convergence analysis of a new hybrid conjugate gradient method for unconstrained optimization problems |
publisher |
Penerbit UTM Press |
publishDate |
2017 |
url |
http://eprints.utm.my/id/eprint/80916/ http://dx.doi.org/10.11113/mjfas.v13n2.540 |
_version_ |
1643658554913062912 |
score |
13.211869 |