  We consider the problem of estimating the inverse covariance matrix by
maximizing the likelihood function with a penalty added to encourage the
sparsity of the resulting matrix. We propose a new approach based on the split
Bregman method to solve the regularized maximum likelihood estimation problem.
We show that our method is significantly faster than the widely used graphical
lasso method, which is based on blockwise coordinate descent, on both
artificial and real-world data. More importantly, different from the graphical
lasso, the split Bregman based method is much more general, and can be applied
to a class of regularization terms other than the $\ell_1$ norm
