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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/114469/ http://psasir.upm.edu.my/id/eprint/114469/1/114469.pdf |
| _version_ | 1848866502972801024 |
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| author | Lim, Keat Hee Leong, Wah June |
| author_facet | Lim, Keat Hee Leong, Wah June |
| author_sort | Lim, Keat Hee |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T14:21:38Z |
| format | Article |
| id | upm-114469 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:21:38Z |
| publishDate | 2024 |
| publisher | Springer Nature |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 |
| spellingShingle | Lim, Keat Hee Leong, Wah June Memoryless quasi-Newton-type methods via some weak secant relations for large-scale unconstrained optimization |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/114469/ http://psasir.upm.edu.my/id/eprint/114469/ http://psasir.upm.edu.my/id/eprint/114469/ http://psasir.upm.edu.my/id/eprint/114469/1/114469.pdf |