Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives

ArXi:2307.09366v2 Announce Type: replace We consider the problem of learning a sparse graph underlying an undirected Gaussian graphical model, a key problem in statistical machine learning. Given $n$ samples from a multivariate Gaussian distribution with $p$ variables, the goal is to estimate the $p \times p$ inverse covariance matrix (aka precision matrix), assuming it is sparse (i.e., has a few nonzero entries