期刊
SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS
卷 30, 期 1, 页码 56-66出版社
SIAM PUBLICATIONS
DOI: 10.1137/060670985
关键词
covariance selection; semidefinite programming; coordinate descent
Given a sample covariance matrix, we solve a maximum likelihood problem penalized by the number of nonzero coefficients in the inverse covariance matrix. Our objective is to find a sparse representation of the sample data and to highlight conditional independence relationships between the sample variables. We first formulate a convex relaxation of this combinatorial problem, we then detail two efficient first-order algorithms with low memory requirements to solve large-scale, dense problem instances.
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