Algorithm 1 GLASSO algorithm [5]

Initialize W = S + λI. Cycle around the columns repeatedly, performing the following steps till convergence: Rearrange the rows/columns so that the target column is last (implicitly). Solve the lasso problem (2.13), using as warm starts the solution from the previous round for this column. Update the row/column (off-diagonal) of the covariance using ${\widehat{\mathbf{w}}}_{12}$ (2.8). Save $\widehat{\beta }$ for this column in the matrix B. Finally, for every row/column, compute the diagonal entries ${\widehat{\theta }}_{\mathit{jj}}$ using (2.14), and convert the B matrix to Θ.