TY - JOUR
T1 - Alternating direction method for covariance selection models
AU - Yuan, Xiaoming
N1 - Funding Information:
Acknowledgements The author would thank Xiangfeng Wang for his help on the numerical experiments. The author was supported by the General Research Fund of Hong Kong No. 203009 and the NSFC grant No. 10701055.
PY - 2012/5
Y1 - 2012/5
N2 - 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.
AB - 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.
KW - Alternating direction method
KW - Covariance selection problem
KW - Log-likelihood
UR - http://www.scopus.com/inward/record.url?scp=84864128199&partnerID=8YFLogxK
U2 - 10.1007/s10915-011-9507-1
DO - 10.1007/s10915-011-9507-1
M3 - Journal article
AN - SCOPUS:84864128199
SN - 0885-7474
VL - 51
SP - 261
EP - 273
JO - Journal of Scientific Computing
JF - Journal of Scientific Computing
IS - 2
ER -