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Title: GENRE (GPU Elastic-Net REgression): A CUDA-Accelerated Package for Massively Parallel Linear Regression with Elastic-Net Regularization
Award ID(s):
1750994
NSF-PAR ID:
10216936
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Journal of Open Source Software
Volume:
5
Issue:
54
ISSN:
2475-9066
Page Range / eLocation ID:
2644
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Norm-1 regularized optimization algorithms are commonly used for Compressive Sensing applications. In this paper, an optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) together with the Elastic Net regularization is presented. This type of regularization is a linear combination of the norm-1 and norm-2 regularizations,allowing a solution between the sparsest and the minimum energy solutions, but still enforcing some sparsivity. The combination of these two regularizations and the distributive capabilities of the ADMM algorithm enables a fast sparse signal recovering with minimum error. 
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