Nodewise Lasso

The nodewise Lasso regresses each design column on the remaining columns and uses its residual to estimate a column of the inverse covariance or inverse Gram matrix.

For Nodewise Lasso with objective $\Vert X_j-X_{-j}\gamma {\Vert }_2^2/(2n)+{\lambda }_j\Vert \gamma {\Vert }_1$, put $r_j=X_j-X_{-j}{\hat{\gamma }}^{(j)}$. Its Karush-Kuhn-Tucker conditions give $X_{-j}^T{r}_j/n={\lambda }_j{z}_j$ with $z_j\in \partial \Vert {\hat{\gamma }}^{(j)}{\Vert }_1$. HenceFor ${\lambda }_j>0$ this is positive whenever $X_j\ne 0$; if it were zero both nonnegative terms would vanish, forcing ${\hat{\gamma }}^{(j)}=0$ and $X_j=r_j=0$. Nonzero columns are therefore an essential hypothesis for division by this quantity.