Subgradient methods for convex minimization
Name
51441857-MIT.pdf
Description
Full printable version
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1015.7 KB
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Checksum (MD5)
388b772eaf1b9cdebdf1972844a0b664
Author(s)
NediÄ , Angelia
Advisor(s)
Dimitri P. Bertsekas.
Date Issued
2002
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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.
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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