The CG-BFGS method for unconstrained optimization problems

In this paper we present a new search direction known as the CG-BFGS method, which uses the search direction of the conjugate gradient method approach in the quasi-Newton methods. The new algorithm is compared with the quasi-Newton methods in terms of the number of iterations and CPU-time. The Broy...

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Main Authors:	Mustafa, Mamat, Ibrahim,, M.A.H.B, June,, L.W., Sofi,, A.Z.M.
Format:	Conference or Workshop Item
Language:	English
Published:	2013
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://eprints.unisza.edu.my/195/1/FH03-FIK-15-03960.jpg http://eprints.unisza.edu.my/195/
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Institution:	Universiti Sultan Zainal Abidin
Language:	English

id	my-unisza-ir.195
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spelling	my-unisza-ir.1952020-10-19T06:58:20Z http://eprints.unisza.edu.my/195/ The CG-BFGS method for unconstrained optimization problems Mustafa, Mamat Ibrahim,, M.A.H.B June,, L.W. Sofi,, A.Z.M. QA75 Electronic computers. Computer science QA76 Computer software In this paper we present a new search direction known as the CG-BFGS method, which uses the search direction of the conjugate gradient method approach in the quasi-Newton methods. The new algorithm is compared with the quasi-Newton methods in terms of the number of iterations and CPU-time. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used as an updating formula for the approximation of the Hessian for both methods. Our numerical analysis provides strong evidence that our CG-BFGS method is more efficient than the ordinary BFGS method. Besides, we also prove that the new algorithm is globally convergent 2013 Conference or Workshop Item NonPeerReviewed image en http://eprints.unisza.edu.my/195/1/FH03-FIK-15-03960.jpg Mustafa, Mamat and Ibrahim,, M.A.H.B and June,, L.W. and Sofi,, A.Z.M. (2013) The CG-BFGS method for unconstrained optimization problems. In: 21st National Symposium on Mathematical Sciences: Germination of Mathematical Sciences Education and Research Towards Global Sustainability, SKSM 21, 6 - 8 November 2013, Penang; Malaysia.
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 QA76 Computer software
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software Mustafa, Mamat Ibrahim,, M.A.H.B June,, L.W. Sofi,, A.Z.M. The CG-BFGS method for unconstrained optimization problems
description	In this paper we present a new search direction known as the CG-BFGS method, which uses the search direction of the conjugate gradient method approach in the quasi-Newton methods. The new algorithm is compared with the quasi-Newton methods in terms of the number of iterations and CPU-time. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used as an updating formula for the approximation of the Hessian for both methods. Our numerical analysis provides strong evidence that our CG-BFGS method is more efficient than the ordinary BFGS method. Besides, we also prove that the new algorithm is globally convergent
format	Conference or Workshop Item
author	Mustafa, Mamat Ibrahim,, M.A.H.B June,, L.W. Sofi,, A.Z.M.
author_facet	Mustafa, Mamat Ibrahim,, M.A.H.B June,, L.W. Sofi,, A.Z.M.
author_sort	Mustafa, Mamat
title	The CG-BFGS method for unconstrained optimization problems
title_short	The CG-BFGS method for unconstrained optimization problems
title_full	The CG-BFGS method for unconstrained optimization problems
title_fullStr	The CG-BFGS method for unconstrained optimization problems
title_full_unstemmed	The CG-BFGS method for unconstrained optimization problems
title_sort	cg-bfgs method for unconstrained optimization problems
publishDate	2013
url	http://eprints.unisza.edu.my/195/1/FH03-FIK-15-03960.jpg http://eprints.unisza.edu.my/195/
_version_	1681493200400809984

The CG-BFGS method for unconstrained optimization problems

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