  Covariance graphical lasso applies a lasso penalty on the elements of the
covariance matrix. This method is useful because it not only produces sparse
estimation of covariance matrix but also discovers marginal independence
structures by generating zeros in the covariance matrix. We propose and explore
two new algorithms for solving the covariance graphical lasso problem. Our new
algorithms are based on coordinate descent and ECM. We show that these two
algorithms are more attractive than the only existing competing algorithm of
Bien and Tibshirani (2011) in terms of simplicity, speed and stability. We also
discuss convergence properties of our algorithms.
