Subgradient methods for convex minimization
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2005
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| Online Access: | http://hdl.handle.net/1721.1/16843 |
| _version_ | 1826208563531350016 |
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| author | NediÄ , Angelia |
| author2 | Dimitri P. Bertsekas. |
| author_facet | Dimitri P. Bertsekas. NediÄ , Angelia |
| author_sort | NediÄ , Angelia |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002. |
| first_indexed | 2024-09-23T14:07:36Z |
| format | Thesis |
| id | mit-1721.1/16843 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T14:07:36Z |
| publishDate | 2005 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/168432019-04-10T09:38:03Z Subgradient methods for convex minimization NediÄ , Angelia Dimitri P. Bertsekas. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002. Includes bibliographical references (p. 169-174). This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Many optimization problems arising in various applications require minimization of an objective cost function that is convex but not differentiable. Such a minimization arises, for example, in model construction, system identification, neural networks, pattern classification, and various assignment, scheduling, and allocation problems. To solve convex but not differentiable problems, we have to employ special methods that can work in the absence of differentiability, while taking the advantage of convexity and possibly other special structures that our minimization problem may possess. In this thesis, we propose and analyze some new methods that can solve convex (not necessarily differentiable) problems. In particular, we consider two classes of methods: incremental and variable metric. by Angelia NediÄ. Ph.D. 2005-05-19T14:59:52Z 2005-05-19T14:59:52Z 2002 2002 Thesis http://hdl.handle.net/1721.1/16843 51441857 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 174 p. 1040674 bytes 1040074 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. NediÄ , Angelia Subgradient methods for convex minimization |
| title | Subgradient methods for convex minimization |
| title_full | Subgradient methods for convex minimization |
| title_fullStr | Subgradient methods for convex minimization |
| title_full_unstemmed | Subgradient methods for convex minimization |
| title_short | Subgradient methods for convex minimization |
| title_sort | subgradient methods for convex minimization |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/16843 |
| work_keys_str_mv | AT nediaangelia subgradientmethodsforconvexminimization |