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Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
Richard Zhang · Salar Fattahi · Somayeh Sojoudi

Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #1
The sparse inverse covariance estimation problem is commonly solved using an $\ell_{1}$-regularized Gaussian maximum likelihood estimator known as “graphical lasso”, but its computational cost becomes prohibitive for large data sets. A recently line of results showed–under mild assumptions–that the graphical lasso estimator can be retrieved by soft-thresholding the sample covariance matrix and solving a maximum determinant matrix completion (MDMC) problem. This paper proves an extension of this result, and describes a Newton-CG algorithm to efficiently solve the MDMC problem. Assuming that the thresholded sample covariance matrix is sparse with a sparse Cholesky factorization, we prove that the algorithm converges to an $\epsilon$-accurate solution in $O(n\log(1/\epsilon))$ time and $O(n)$ memory. The algorithm is highly efficient in practice: we solve the associated MDMC problems with as many as 200,000 variables to 7-9 digits of accuracy in less than an hour on a standard laptop computer running MATLAB.

Author Information

Richard Zhang (University of California, Berkeley)
Salar Fattahi (UC Berkeley)
Somayeh Sojoudi (University of California, Berkeley)

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