We propose a scalable, efficient and statistically motivated computational
framework for Graphical Lasso (Friedman et al., 2007b) - a covariance
regularization framework that has received significant attention in the
statistics community over the past few years. Existing algorithms have trouble
in scaling to dimensions larger than a thousand. Our proposal significantly
enhances the state-of-the-art for such moderate sized problems and gracefully
scales to larger problems where other algorithms become practically infeasible.
This requires a few key new ideas.