  We introduce GGLasso, a Python package for solving General Graphical Lasso
problems. The Graphical Lasso scheme, introduced by (Friedman 2007) (see also
(Yuan 2007; Banerjee 2008)), estimates a sparse inverse covariance matrix
$\Theta$ from multivariate Gaussian data $\mathcal{X} \sim \mathcal{N}(\mu,
\Sigma) \in \mathbb{R}^p$. Originally proposed by (Dempster 1972) under the
name Covariance Selection, this estimation framework has been extended to
include latent variables in (Chandrasekaran 2012). Recent extensions also
include the joint estimation of multiple inverse covariance matrices, see,
e.g., in (Danaher 2013; Tomasi 2018). The GGLasso package contains methods for
solving a general problem formulation, including important special cases, such
as, the single (latent variable) Graphical Lasso, the Group, and the Fused
Graphical Lasso.
