A class of diagonal quasi-newton methods for large-scale convex minimization
We study the convergence properties of a class of low memory methods for solving large-scale unconstrained problems. This class of methods belongs to that of quasi-Newton family, except for which the approximation to Hessian, at each step, is updated by means of a diagonal matrix. Using appropriate...
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| Format: | Article |
| Language: | English |
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USM Publishing
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/43466/1/abstract01.pdf |
| _version_ | 1825929368534253568 |
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| author | Leong, Wah June |
| author_facet | Leong, Wah June |
| author_sort | Leong, Wah June |
| collection | UPM |
| description | We study the convergence properties of a class of low memory methods for solving large-scale unconstrained problems. This class of methods belongs to that of quasi-Newton family, except for which the approximation to Hessian, at each step, is updated by means of a diagonal matrix. Using appropriate scaling, we show that the methods can be implemented so as to be globally and \(R\) -linearly convergent with standard inexact line searches. Preliminary numerical results suggest that the methods are good alternative to other low memory methods such as the CG and spectral gradient methods. |
| first_indexed | 2024-03-06T08:55:45Z |
| format | Article |
| id | upm.eprints-43466 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:55:45Z |
| publishDate | 2015 |
| publisher | USM Publishing |
| record_format | dspace |
| spelling | upm.eprints-434662016-06-28T08:04:50Z http://psasir.upm.edu.my/id/eprint/43466/ A class of diagonal quasi-newton methods for large-scale convex minimization Leong, Wah June We study the convergence properties of a class of low memory methods for solving large-scale unconstrained problems. This class of methods belongs to that of quasi-Newton family, except for which the approximation to Hessian, at each step, is updated by means of a diagonal matrix. Using appropriate scaling, we show that the methods can be implemented so as to be globally and \(R\) -linearly convergent with standard inexact line searches. Preliminary numerical results suggest that the methods are good alternative to other low memory methods such as the CG and spectral gradient methods. USM Publishing 2015-04 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/43466/1/abstract01.pdf Leong, Wah June (2015) A class of diagonal quasi-newton methods for large-scale convex minimization. Bulletin of the Malaysian Mathematical Sciences Society. pp. 1-14. ISSN 0126-6705 10.1007/s40840-015-0117-1 |
| spellingShingle | Leong, Wah June A class of diagonal quasi-newton methods for large-scale convex minimization |
| title | A class of diagonal quasi-newton methods for large-scale convex minimization |
| title_full | A class of diagonal quasi-newton methods for large-scale convex minimization |
| title_fullStr | A class of diagonal quasi-newton methods for large-scale convex minimization |
| title_full_unstemmed | A class of diagonal quasi-newton methods for large-scale convex minimization |
| title_short | A class of diagonal quasi-newton methods for large-scale convex minimization |
| title_sort | class of diagonal quasi newton methods for large scale convex minimization |
| url | http://psasir.upm.edu.my/id/eprint/43466/1/abstract01.pdf |
| work_keys_str_mv | AT leongwahjune aclassofdiagonalquasinewtonmethodsforlargescaleconvexminimization AT leongwahjune classofdiagonalquasinewtonmethodsforlargescaleconvexminimization |