# Graphical Lasso¶


This tour details the graphical Lasso method for covariance estimation. The idea of using sparsity of the precision matrix to regularize covariance estimation appears in several works, including [MeinshausenBuhlmann06]. The use of an $\ell^1$ penalty, together with the block-coordinate optimization scheme presented here, is introduced in [AspremontBanerjeeElGhaoui08,BanerjeeElGhaouiAspremont08]. This algorithm was popularized by [FriedmanHastieTibshirani08] under the name "Graphical Lasso".

In [1]:
import numpy as np
import matplotlib.pyplot as plt


# Bibliography¶

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