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We consider the problem of computing a positive definite p × p inverse covariance matrix aka precision matrix θ=(θ_ij) which optimizes a regularized Gaussian maximum likelihood problem, with the elastic-net regularizer ∑_i,j=1^pλ (α|θ_ij| + 1/2(1- α) θ_ij^2), with regularization parameters α∈ [0,1] and λ>0. The associated convex semidefinite optimization problem is notoriously difficult to scale to large problems and has demanded significant attention over the past several years. We propose aarXiv:1509.00426v1 fatcat:nwlyl2oiyzhsbmeyeit6bkopbm