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An accelerated dual method for SPGL1 Bao, Anyi
Abstract
This thesis studies a widely-used solver SPGL1, which applies a general root-finding process to solve the basis pursuit denoising problem. This process involves a nested loop. The outer loop is an inexact Newton root-finding process, and the inner loop approximately solves LASSO (least absolute shrinkage and selection operator). We propose an accelerated dual method to accelerate the inner loop by optimizing the dual problem of LASSO on a low-dimensional space. Experimental results show that our accelerated method that can successfully reduce the total iteration counts. Our future work is to reduce the total running time of our accelerated method.
Item Metadata
Title |
An accelerated dual method for SPGL1
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2019
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Description |
This thesis studies a widely-used solver SPGL1, which applies a general root-finding process to solve the basis pursuit denoising problem. This process involves a nested loop. The outer loop is an inexact Newton root-finding process, and the inner loop approximately solves LASSO (least absolute shrinkage and selection operator). We propose an accelerated dual method to accelerate the inner loop by optimizing the dual problem of LASSO on a low-dimensional space. Experimental results show that our accelerated method that can successfully reduce the total iteration counts. Our future work is to reduce the total running time of our accelerated method.
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Genre | |
Type | |
Language |
eng
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Date Available |
2019-04-12
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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 |
10.14288/1.0378176
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2019-05
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International