  This paper studies the partial estimation of Gaussian graphical models from
high-dimensional empirical observations. We derive a convex formulation for
this problem using $\ell_1$-regularized maximum-likelihood estimation, which
can be solved via a block coordinate descent algorithm. Statistical estimation
performance can be established for our method. The proposed approach has
competitive empirical performance compared to existing methods, as demonstrated
by various experiments on synthetic and real datasets.
