A conjugate gradient method with Strong Wolfe-Powell line search for unconstrained optimization

A conjugate gradient method with Strong Wolfe-Powell line search for unconstrained optimization

In this paper, a modified conjugate gradient method is presented for solving large-scale unconstrained optimization problems, which possesses the sufficient descent property with Strong Wolfe-Powell line search. A global convergence result was proved when the (SWP) line search was used under some co...

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Main Authors: Mustafa, Mamat, Mohamed, Hamoda, Mohd Rivaie, Mohd Ali
Format: Article
Language:English
Published: Hikari Ltd. 2016
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.unisza.edu.my/7219/1/FH02-FIK-16-05684.jpg
http://eprints.unisza.edu.my/7219/
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Institution: Universiti Sultan Zainal Abidin
Language: English
id my-unisza-ir.7219
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spelling my-unisza-ir.72192022-09-13T05:47:15Z http://eprints.unisza.edu.my/7219/ A conjugate gradient method with Strong Wolfe-Powell line search for unconstrained optimization Mustafa, Mamat Mohamed, Hamoda Mohd Rivaie, Mohd Ali QA75 Electronic computers. Computer science In this paper, a modified conjugate gradient method is presented for solving large-scale unconstrained optimization problems, which possesses the sufficient descent property with Strong Wolfe-Powell line search. A global convergence result was proved when the (SWP) line search was used under some conditions. Computational results for a set consisting of 138 unconstrained optimization test problems showed that this new conjugate gradient algorithm seems to converge more stable and is superior to other similar methods in many situations. Hikari Ltd. 2016 Article PeerReviewed image en http://eprints.unisza.edu.my/7219/1/FH02-FIK-16-05684.jpg Mustafa, Mamat and Mohamed, Hamoda and Mohd Rivaie, Mohd Ali (2016) A conjugate gradient method with Strong Wolfe-Powell line search for unconstrained optimization. Applied Mathematical Sciences, 10 (13). pp. 721-734. ISSN 1312885X [P]
institution Universiti Sultan Zainal Abidin
building UNISZA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sultan Zainal Abidin
content_source UNISZA Institutional Repository
url_provider https://eprints.unisza.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mustafa, Mamat
Mohamed, Hamoda
Mohd Rivaie, Mohd Ali
A conjugate gradient method with Strong Wolfe-Powell line search for unconstrained optimization
description In this paper, a modified conjugate gradient method is presented for solving large-scale unconstrained optimization problems, which possesses the sufficient descent property with Strong Wolfe-Powell line search. A global convergence result was proved when the (SWP) line search was used under some conditions. Computational results for a set consisting of 138 unconstrained optimization test problems showed that this new conjugate gradient algorithm seems to converge more stable and is superior to other similar methods in many situations.
format Article
author Mustafa, Mamat
Mohamed, Hamoda
Mohd Rivaie, Mohd Ali
author_facet Mustafa, Mamat
Mohamed, Hamoda
Mohd Rivaie, Mohd Ali
author_sort Mustafa, Mamat
title A conjugate gradient method with Strong Wolfe-Powell line search for unconstrained optimization
title_short A conjugate gradient method with Strong Wolfe-Powell line search for unconstrained optimization
title_full A conjugate gradient method with Strong Wolfe-Powell line search for unconstrained optimization
title_fullStr A conjugate gradient method with Strong Wolfe-Powell line search for unconstrained optimization
title_full_unstemmed A conjugate gradient method with Strong Wolfe-Powell line search for unconstrained optimization
title_sort conjugate gradient method with strong wolfe-powell line search for unconstrained optimization
publisher Hikari Ltd.
publishDate 2016
url http://eprints.unisza.edu.my/7219/1/FH02-FIK-16-05684.jpg
http://eprints.unisza.edu.my/7219/
_version_ 1744358593067483136

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