2026-09-19T02:04:23Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/168432022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Dimitri P. Bertsekas.
NediÄ , Angelia
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-05-19T14:59:52Z
2005-05-19T14:59:52Z
2002
2002
http://hdl.handle.net/1721.1/16843
51441857
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.
174 p.
1040674 bytes
1040074 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Subgradient methods for convex minimization
Thesis
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