Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Large-Scale Sparse Inverse Covariance Estimation via Thresholding and
  Max-Det Matrix Completion   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 recent 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.

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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
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The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: mtrl-th