A note on the lack of symmetry in the graphical lasso

Abstract

The graphical lasso (glasso) is a widely-used fast algorithm for estimating sparse inverse covariance matrices. The glasso solves an L1 penalized maximum likelihood problem and is available as an R library on CRAN. The output from the glasso, a regularized covariance matrix estimate a sparse inverse covariance matrix estimate, not only identify a graphical model but can also serve as intermediate inputs into multivariate procedures such as PCA, LDA, MANOVA, and others. The glasso indeed produces a covariance matrix estimate which solves the L1 penalized optimization problem in a dual sense; however, the method for producing the inverse covariance matrix estimator after this optimization is inexact and may produce asymmetric estimates. This problem is exacerbated when the amount of L1 regularization that is applied is small, which in turn is more likely to occur if the true underlying inverse covariance matrix is not sparse. The lack of symmetry can potentially have consequences. First, it implies that the covariance and inverse covariance estimates are not numerical inverses of one another, and second, asymmetry can possibly lead to negative or complex eigenvalues,rendering many multivariate procedures which may depend on the inverse covariance estimator unusable. We demonstrate this problem, explain its causes, and propose possible remedies.