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Memoryless quasi-Newton-type methods via some weak secant relations for large-scale unconstrained optimization

This article presents two variants of memoryless quasi-Newton methods with backtracking line search for large-scale unconstrained minimization. These updating methods are derived by means of a least-change updating strategy subjected to some weaker form of secant relation obtained by projecting the...

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Main Authors: Lim, Keat Hee, Leong, Wah June
Format: Article
Language:English
Published: Springer Nature 2024
Online Access:http://psasir.upm.edu.my/id/eprint/114469/1/114469.pdf
http://psasir.upm.edu.my/id/eprint/114469/
https://journalofinequalitiesandapplications.springeropen.com/articles/10.1186/s13660-024-03240-z
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spelling my.upm.eprints.1144692025-01-20T02:42:53Z http://psasir.upm.edu.my/id/eprint/114469/ Memoryless quasi-Newton-type methods via some weak secant relations for large-scale unconstrained optimization Lim, Keat Hee Leong, Wah June This article presents two variants of memoryless quasi-Newton methods with backtracking line search for large-scale unconstrained minimization. These updating methods are derived by means of a least-change updating strategy subjected to some weaker form of secant relation obtained by projecting the secant equation onto the search direction. In such a setting, the search direction can be computed without the need of calculation and storage of matrices. We establish the convergence properties for these methods, and their performance is tested on a large set of test functions by comparing with standard methods of similar computational cost and storage requirement. Our numerical results indicate that significant improvement has been achieved with respect to iteration counts and number of function evaluations. Springer Nature 2024 Article PeerReviewed text en cc_by_nc_nd_4 http://psasir.upm.edu.my/id/eprint/114469/1/114469.pdf Lim, Keat Hee and Leong, Wah June (2024) Memoryless quasi-Newton-type methods via some weak secant relations for large-scale unconstrained optimization. Journal of Inequalities and Applications, 2024 (1). art. no. 155. pp. 1-17. ISSN 1025-5834; eISSN: 1029-242X https://journalofinequalitiesandapplications.springeropen.com/articles/10.1186/s13660-024-03240-z 10.1186/s13660-024-03240-z
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description This article presents two variants of memoryless quasi-Newton methods with backtracking line search for large-scale unconstrained minimization. These updating methods are derived by means of a least-change updating strategy subjected to some weaker form of secant relation obtained by projecting the secant equation onto the search direction. In such a setting, the search direction can be computed without the need of calculation and storage of matrices. We establish the convergence properties for these methods, and their performance is tested on a large set of test functions by comparing with standard methods of similar computational cost and storage requirement. Our numerical results indicate that significant improvement has been achieved with respect to iteration counts and number of function evaluations.
format Article
author Lim, Keat Hee
Leong, Wah June
spellingShingle Lim, Keat Hee
Leong, Wah June
Memoryless quasi-Newton-type methods via some weak secant relations for large-scale unconstrained optimization
author_facet Lim, Keat Hee
Leong, Wah June
author_sort Lim, Keat Hee
title Memoryless quasi-Newton-type methods via some weak secant relations for large-scale unconstrained optimization
title_short Memoryless quasi-Newton-type methods via some weak secant relations for large-scale unconstrained optimization
title_full Memoryless quasi-Newton-type methods via some weak secant relations for large-scale unconstrained optimization
title_fullStr Memoryless quasi-Newton-type methods via some weak secant relations for large-scale unconstrained optimization
title_full_unstemmed Memoryless quasi-Newton-type methods via some weak secant relations for large-scale unconstrained optimization
title_sort memoryless quasi-newton-type methods via some weak secant relations for large-scale unconstrained optimization
publisher Springer Nature
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/114469/1/114469.pdf
http://psasir.upm.edu.my/id/eprint/114469/
https://journalofinequalitiesandapplications.springeropen.com/articles/10.1186/s13660-024-03240-z
_version_ 1823093164515262464
score 13.244413

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