Alternating Direction Method for Covariance Selection Models

Alternating Direction Method for Covariance Selection Models

The covariance selection problem captures many applications in various fields, and it has been well studied in the literature. Recently, an l 1 -norm penalized log-likelihood model has been developed for the covariance selection problem, and this novel model is capable of completing the model select...

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Veröffentlicht in:Journal of scientific computing 2012-05, Vol.51 (2), p.261-273
1. Verfasser: Yuan, Xiaoming
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Sprache:eng
Schlagworte:
Algorithms
Computational Mathematics and Numerical Analysis
Covariance
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical models
Mathematics
Mathematics and Statistics
Parameter estimation
Theoretical
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description The covariance selection problem captures many applications in various fields, and it has been well studied in the literature. Recently, an l 1 -norm penalized log-likelihood model has been developed for the covariance selection problem, and this novel model is capable of completing the model selection and parameter estimation simultaneously. With the rapidly increasing magnitude of data, it is urged to consider efficient numerical algorithms for large-scale cases of the l 1 -norm penalized log-likelihood model. For this purpose, this paper develops the alternating direction method (ADM). Some preliminary numerical results show that the ADM approach is very efficient for large-scale cases of the l 1 -norm penalized log-likelihood model.
doi_str_mv 10.1007/s10915-011-9507-1
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subjects Algorithms
Computational Mathematics and Numerical Analysis
Covariance
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical models
Mathematics
Mathematics and Statistics
Parameter estimation
Theoretical
title Alternating Direction Method for Covariance Selection Models
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