New quasi-Newton methods via higher order tensor models.
Many researches attempt to improve the efficiency of the usual quasi-Newton (QN) methods by accelerating the performance of the algorithm without causing more storage demand. They aim to employ more available information from the function values and gradient to approximate the curvature of the objec...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Elsevier
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/24642/1/New%20quasi.pdf |
| _version_ | 1825925612869517312 |
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| author | Biglari, Fahmeh Abu Hassan, Malik Leong, Wah June |
| author_facet | Biglari, Fahmeh Abu Hassan, Malik Leong, Wah June |
| author_sort | Biglari, Fahmeh |
| collection | UPM |
| description | Many researches attempt to improve the efficiency of the usual quasi-Newton (QN) methods by accelerating the performance of the algorithm without causing more storage demand. They aim to employ more available information from the function values and gradient to approximate the curvature of the objective function. In this paper we derive a new QN method of this type using a fourth order tensor model and show that it is superior with respect to the prior modification of Wei et al. (2006) [4]. Convergence analysis gives the local convergence property of this method and numerical results show the advantage of the modified QN method. |
| first_indexed | 2024-03-06T08:00:27Z |
| format | Article |
| id | upm.eprints-24642 |
| institution | Universiti Putra Malaysia |
| language | English English |
| last_indexed | 2024-03-06T08:00:27Z |
| publishDate | 2011 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-246422015-08-26T06:39:58Z http://psasir.upm.edu.my/id/eprint/24642/ New quasi-Newton methods via higher order tensor models. Biglari, Fahmeh Abu Hassan, Malik Leong, Wah June Many researches attempt to improve the efficiency of the usual quasi-Newton (QN) methods by accelerating the performance of the algorithm without causing more storage demand. They aim to employ more available information from the function values and gradient to approximate the curvature of the objective function. In this paper we derive a new QN method of this type using a fourth order tensor model and show that it is superior with respect to the prior modification of Wei et al. (2006) [4]. Convergence analysis gives the local convergence property of this method and numerical results show the advantage of the modified QN method. Elsevier 2011-02-15 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/24642/1/New%20quasi.pdf Biglari, Fahmeh and Abu Hassan, Malik and Leong, Wah June (2011) New quasi-Newton methods via higher order tensor models. Journal of Computational and Applied Mathematics, 235 (8). pp. 2412-2422. ISSN 0377-0427 http://www.elsevier.com/ 10.1016/j.cam.2010.10.041 English |
| spellingShingle | Biglari, Fahmeh Abu Hassan, Malik Leong, Wah June New quasi-Newton methods via higher order tensor models. |
| title | New quasi-Newton methods via higher order tensor models. |
| title_full | New quasi-Newton methods via higher order tensor models. |
| title_fullStr | New quasi-Newton methods via higher order tensor models. |
| title_full_unstemmed | New quasi-Newton methods via higher order tensor models. |
| title_short | New quasi-Newton methods via higher order tensor models. |
| title_sort | new quasi newton methods via higher order tensor models |
| url | http://psasir.upm.edu.my/id/eprint/24642/1/New%20quasi.pdf |
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