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1.  Sparse inverse covariance estimation with the graphical lasso 
Biostatistics (Oxford, England)  2007;9(3):432-441.
SUMMARY
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm—the graphical lasso—that is remarkably fast: It solves a 1000-node problem (~500 000 parameters) in at most a minute and is 30–4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.
doi:10.1093/biostatistics/kxm045
PMCID: PMC3019769  PMID: 18079126
Gaussian covariance; Graphical model; L1; Lasso
2.  Sparse inverse covariance estimation with the graphical lasso 
Biostatistics (Oxford, England)  2007;9(3):432-441.
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm—the graphical lasso—that is remarkably fast: It solves a 1000-node problem (∼500000 parameters) in at most a minute and is 30–4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.
doi:10.1093/biostatistics/kxm045
PMCID: PMC3019769  PMID: 18079126
Gaussian covariance; Graphical model; L1; Lasso

Results 1-2 (2)