  Given a sample covariance matrix, we solve a maximum likelihood problem
penalized by the number of nonzero coefficients in the inverse covariance
matrix. Our objective is to find a sparse representation of the sample data and
to highlight conditional independence relationships between the sample
variables. We first formulate a convex relaxation of this combinatorial
problem, we then detail two efficient first-order algorithms with low memory
requirements to solve large-scale, dense problem instances.
