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We consider inverse covariance estimation with group sparsity. The groups areoverlapping principal submatrices, which may correspond to structural similarity(e.g. pixels in adjacent regions) or categories (e.g. voter party loyalties). Wepropose a scalable method that makes use of chordal decomposition and appliesthe Frank-Wolfe algorithm. For small simulated problems with block sparsity, weoften perform better than vanilla LASSO. We also apply the method on votingrecords of various congress members, with gender and party as groups
