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Hierarchical Convex Optimization with Proximal Splitting Operators Yamada, Isao
Description
The proximal splitting algorithms can iteratively approximate an unspecial vector among possibly infinitely many minimizers of a superposition of multiple nonsmooth convex functions.
With elegant translations of the solution set, i.e., the set of all minimizers, into the fixed point sets of nonexpansive mappings,
the hybrid steepest descent method allows further strategic selection of a most desirable vector among the solution set, by minimizing an additional convex function over the solution set.
In this talk, we introduce fixed point theoretic interpretations of variety of proximal splitting algorithms and their enhancements by the hybrid steepest descent method with applications to recent advanced statistical estimation problems.
Item Metadata
Title |
Hierarchical Convex Optimization with Proximal Splitting Operators
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2017-09-19T15:01
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Description |
The proximal splitting algorithms can iteratively approximate an unspecial vector among possibly infinitely many minimizers of a superposition of multiple nonsmooth convex functions.
With elegant translations of the solution set, i.e., the set of all minimizers, into the fixed point sets of nonexpansive mappings, the hybrid steepest descent method allows further strategic selection of a most desirable vector among the solution set, by minimizing an additional convex function over the solution set. In this talk, we introduce fixed point theoretic interpretations of variety of proximal splitting algorithms and their enhancements by the hybrid steepest descent method with applications to recent advanced statistical estimation problems. |
Extent |
32 minutes
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: Tokyo Institute of Technology
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Series | |
Date Available |
2018-03-25
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0364454
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Faculty
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International