 # ⓘ Graphical lasso. In statistics, the graphical lasso is a sparse penalized maximum likelihood estimator for the concentration or precision matrix of a multivaria ..

## ⓘ Graphical lasso

In statistics, the graphical lasso is a sparse penalized maximum likelihood estimator for the concentration or precision matrix of a multivariate elliptical distribution. The original variant was formulated to solve Dempsters covariance selection problem for the multivariate Gaussian distribution when observations were limited. Subsequently, the optimization algorithms to solve this problem were improved and extended to other types of estimators and distributions.

## 1. Setting

Consider observations X 1, X 2, …, X n {\displaystyle X_{1},X_{2},\ldots,X_{n}} from multivariate Gaussian distribution X ∼ N 0, Σ {\displaystyle X\sim N0,\Sigma}. We are interested in estimating the precision matrix Θ = Σ − 1 {\displaystyle \Theta =\Sigma ^{-1}}.

The graphical lasso estimator is the Θ ^ {\displaystyle {\hat {\Theta }}} such that:

Θ ^ = argmin Θ ≥ 0 ⁡ tr ⁡ S Θ − log ⁡ det Θ + λ ∑ j ≠ k | Θ j k |) {\displaystyle {\hat {\Theta }}=\operatorname {argmin} _{\Theta \geq 0}\left\operatorname {tr} S\Theta-\log \det\Theta+\lambda \sum _{j\neq k}|\Theta _{jk}|\right)}

where S {\displaystyle S} is the sample covariance, and λ {\displaystyle \lambda } is the penalizing parameter.

## 2. Application

To obtain the estimator in programs, users could use the R package glasso, GraphicalLasso class in Python Scikit-Learn package, or the skggm Python package similar to scikit-learn

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