Block-enhanced precision matrix estimation for large-scale datasets

This work presents a novel l1-regularized maximum likelihood method for performant large-scale sparse precision matrix estimation utilizing the block structures in the underlying computations, and demonstrates the effectiveness, accuracy, and performance of these algorithms.