The hybrid BFGS-CG method in solving unconstrained optimization problems
type: Item
instance of: scholarly article
title: The hybrid BFGS-CG method in solving unconstrained optimization problems
zbMATH Open document ID: 1470.90159
DOI: 10.1155/2014/507102
author: Mohd Asrul Hery Ibrahim
author: Mustafa Mamat
author: W. J. Leong
published in: Abstract and Applied Analysis
publication date: 2019-02-14T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 90C53
Mathematics Subject Classification ID: 90C30
zbMATH DE Number: 7022508
describes a project that uses: Genocop
describes a project that uses: minpack
describes a project that uses: SCALCG
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/507102
OpenAlex ID: W2068557887
Wikidata QID: Q59038891
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description: scientific article; zbMATH DE number 7022508