  Covariance selection seeks to estimate a covariance matrix by maximum
likelihood while restricting the number of nonzero inverse covariance matrix
coefficients. A single penalty parameter usually controls the tradeoff between
log likelihood and sparsity in the inverse matrix. We describe an efficient
algorithm for computing a full regularization path of solutions to this
problem.
