Diagonal quasi-Newton methods via least change updating principle with weighted Frobenius norm

This paper presents a class of low memory quasi-Newton methods with standard backtracking line search for large-scale unconstrained minimization. The methods are derived by means of least change updating technique analogous to that for the DFP method except that the full quasi-Newton matrix has b...

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Main Authors: Leong, Wah June, Enshaei, Sharareh, Kek, Sie Long
Format: Article
Language:English
Published: Springer 2021
Subjects:
Online Access:http://eprints.uthm.edu.my/1127/
http://eprints.uthm.edu.my/1127/1/J12010_5a7e0282f614f0890c3fed53dbfc3ddd.pdf
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author Leong, Wah June
Enshaei, Sharareh
Kek, Sie Long
author_facet Leong, Wah June
Enshaei, Sharareh
Kek, Sie Long
author_sort Leong, Wah June
building UTHM Institutional Repository
collection Online Access
description This paper presents a class of low memory quasi-Newton methods with standard backtracking line search for large-scale unconstrained minimization. The methods are derived by means of least change updating technique analogous to that for the DFP method except that the full quasi-Newton matrix has been replaced by some diagonal matrix. We establish convergence properties for some particular members of the class under line search with Armijo condition. Sufficient conditions for the methods to be superlinearly convergent are also given. Numerical results are then presented to illustrate the usefulness of these methods in large-scale minimization.
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institution Universiti Tun Hussein Onn Malaysia
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publishDate 2021
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spelling uthm-11272021-10-17T07:48:55Z http://eprints.uthm.edu.my/1127/ Diagonal quasi-Newton methods via least change updating principle with weighted Frobenius norm Leong, Wah June Enshaei, Sharareh Kek, Sie Long T Technology (General) This paper presents a class of low memory quasi-Newton methods with standard backtracking line search for large-scale unconstrained minimization. The methods are derived by means of least change updating technique analogous to that for the DFP method except that the full quasi-Newton matrix has been replaced by some diagonal matrix. We establish convergence properties for some particular members of the class under line search with Armijo condition. Sufficient conditions for the methods to be superlinearly convergent are also given. Numerical results are then presented to illustrate the usefulness of these methods in large-scale minimization. Springer 2021 Article PeerReviewed text en http://eprints.uthm.edu.my/1127/1/J12010_5a7e0282f614f0890c3fed53dbfc3ddd.pdf Leong, Wah June and Enshaei, Sharareh and Kek, Sie Long (2021) Diagonal quasi-Newton methods via least change updating principle with weighted Frobenius norm. Numerical Algorithms, 86. pp. 1225-1241. https://doi.org/10.1007/s11075-020-00930-9
spellingShingle T Technology (General)
Leong, Wah June
Enshaei, Sharareh
Kek, Sie Long
Diagonal quasi-Newton methods via least change updating principle with weighted Frobenius norm
title Diagonal quasi-Newton methods via least change updating principle with weighted Frobenius norm
title_full Diagonal quasi-Newton methods via least change updating principle with weighted Frobenius norm
title_fullStr Diagonal quasi-Newton methods via least change updating principle with weighted Frobenius norm
title_full_unstemmed Diagonal quasi-Newton methods via least change updating principle with weighted Frobenius norm
title_short Diagonal quasi-Newton methods via least change updating principle with weighted Frobenius norm
title_sort diagonal quasi-newton methods via least change updating principle with weighted frobenius norm
topic T Technology (General)
url http://eprints.uthm.edu.my/1127/
http://eprints.uthm.edu.my/1127/
http://eprints.uthm.edu.my/1127/1/J12010_5a7e0282f614f0890c3fed53dbfc3ddd.pdf