Department of Mathematics
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 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 l1-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 l1-norm penalized log-likelihood model. © Springer Science+Business Media, LLC 2011. © Springer Science+Business Media, LLC 2011.
Alternating direction method, Covariance selection problem, Log-likelihood
Source Publication Title
Journal of Scientific Computing
Link to Publisher's Edition
Yuan, X. (2012). Alternating direction method for covariance selection models. Journal of Scientific Computing, 51 (2), 261-273. https://doi.org/10.1007/s10915-011-9507-1