1abb1e1ea5f481b589da52303b091cbb-Reviews.html

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper describes an approach to derive the L1 regularized Gaussian maximum likelihood estimator for the sparse inverse covariance estimation problem. The focus of this paper was to scale the previous algorithm QUIC to solve problems involving million of variables. They describe three innovations brought about by this new approach: inexact Hessians, better computation of the logdet function, and carefully selecting the blocks updated in their block coordinate scheme via a smart clustering scheme. The numerical results test their new method against the previous QUIC algorithm, GLASSO and ALM, showing improved performance on a few select problems.