GGLasso - a Python package for General Graphical Lasso computation

We introduce GGLasso, a Python package for solving General Graphical Lasso problems. The Graphical Lasso scheme, introduced by Friedman et al. (2007) (see also Yuan & Lin (2007), Banerjee et al. (2008)), estimates a sparse inverse covariance matrix Θ from multivariate Gaussian data X ∼ N (μ,Σ) ∈ Rp. Originally proposed by Dempster (1972) under the name Covariance Selection, this estimation framework has been extended to include latent variables in Chandrasekaran et al. (2012). Recent extensions also include the joint estimation of multiple inverse covariance matrices, see, e.g., in Danaher et al. (2013), Tomasi et al. (2018). The GGLasso package contains methods for solving the general problem formulation:

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