all, I'm doing a graphical lasso in order to approximate the inverse of the covariance matrix of a 1200 (p-features) by 100 or so (n observations) data matrix. Basically, I'm inverting a 1200 x 1200 covariance matrix using the glasso function from the glasso package in R. So far it has taken 48 hours and this is just not acceptable. The rho coefficient is set to 0.25, which I think is pretty generous.
Can anyone help me understand how I can speed this up? Is there a better implementation out there? I understand complexity concerns, but I do not have an idea about if this method is just slow because of inefficient code, or if I need to do feature selection before I do the covariance matrix and then the graphical LASSO.
Looking forward to your thoughts!