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The hybrid BFGS-CG method in solving unconstrained optimization problems

In solving large scale problems, the quasi-Newton method is known as the most efficient method in solving unconstrained optimization problems. Hence, a new hybrid method, known as the BFGS-CG method, has been created based on these properties, combining the search direction between conjugate gradien...

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Main Authors: Mustafa, Mamat, Mohd Asrul Hery, Ibrahim, Wah June, Leong
Format: Article
Language:English
English
Published: Hindawi Publishing Corporation 2014
Subjects:
HA Statistics
QA Mathematics
Online Access:http://eprints.unisza.edu.my/4936/1/FH02-FIK-14-00730.jpg
http://eprints.unisza.edu.my/4936/2/FH02-FIK-14-02096.jpg
http://eprints.unisza.edu.my/4936/
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spelling my-unisza-ir.49362022-09-13T05:49:32Z http://eprints.unisza.edu.my/4936/ The hybrid BFGS-CG method in solving unconstrained optimization problems Mustafa, Mamat Mohd Asrul Hery, Ibrahim Wah June, Leong HA Statistics QA Mathematics In solving large scale problems, the quasi-Newton method is known as the most efficient method in solving unconstrained optimization problems. Hence, a new hybrid method, known as the BFGS-CG method, has been created based on these properties, combining the search direction between conjugate gradient methods and quasi-Newton methods. In comparison to standard BFGS methods and conjugate gradient methods, the BFGS-CG method shows significant improvement in the total number of iterations and CPU time required to solve large scale unconstrained optimization problems. We also prove that the hybrid method is globally convergent. Hindawi Publishing Corporation 2014 Article PeerReviewed image en http://eprints.unisza.edu.my/4936/1/FH02-FIK-14-00730.jpg image en http://eprints.unisza.edu.my/4936/2/FH02-FIK-14-02096.jpg Mustafa, Mamat and Mohd Asrul Hery, Ibrahim and Wah June, Leong (2014) The hybrid BFGS-CG method in solving unconstrained optimization problems. Abstract and Applied Analysis, 2014. pp. 1-6. ISSN 16870409
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
English
topic HA Statistics
QA Mathematics
spellingShingle HA Statistics
QA Mathematics
Mustafa, Mamat
Mohd Asrul Hery, Ibrahim
Wah June, Leong
The hybrid BFGS-CG method in solving unconstrained optimization problems
description In solving large scale problems, the quasi-Newton method is known as the most efficient method in solving unconstrained optimization problems. Hence, a new hybrid method, known as the BFGS-CG method, has been created based on these properties, combining the search direction between conjugate gradient methods and quasi-Newton methods. In comparison to standard BFGS methods and conjugate gradient methods, the BFGS-CG method shows significant improvement in the total number of iterations and CPU time required to solve large scale unconstrained optimization problems. We also prove that the hybrid method is globally convergent.
format Article
author Mustafa, Mamat
Mohd Asrul Hery, Ibrahim
Wah June, Leong
author_facet Mustafa, Mamat
Mohd Asrul Hery, Ibrahim
Wah June, Leong
author_sort Mustafa, Mamat
title The hybrid BFGS-CG method in solving unconstrained optimization problems
title_short The hybrid BFGS-CG method in solving unconstrained optimization problems
title_full The hybrid BFGS-CG method in solving unconstrained optimization problems
title_fullStr The hybrid BFGS-CG method in solving unconstrained optimization problems
title_full_unstemmed The hybrid BFGS-CG method in solving unconstrained optimization problems
title_sort hybrid bfgs-cg method in solving unconstrained optimization problems
publisher Hindawi Publishing Corporation
publishDate 2014
url http://eprints.unisza.edu.my/4936/1/FH02-FIK-14-00730.jpg
http://eprints.unisza.edu.my/4936/2/FH02-FIK-14-02096.jpg
http://eprints.unisza.edu.my/4936/
_version_ 1744358518621732864
score 13.211869

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