Split Bregman Method for Sparse Inverse Covariance Estimation with Matrix Iteration Acceleration
xhx@ics.uci.eduAbstract: We consider the problem of estimating the inverse covariance matrix by maximizing thelikelihood function with a penalty added to encourage the sparsity of the resulting matrix. We proposea new approach based on the split Bregman method to solve the regularized maximum likelihoodestimation problem. We show that our method is signiﬁcantly faster than the widely used graphicallasso 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 moregeneral, and can be applied to a class of regularization terms other than the l