Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exact covariance thresholding into connected components for large-scale
  Graphical Lasso   We consider the sparse inverse covariance regularization problem or graphical
lasso with regularization parameter $\rho$. Suppose the co- variance graph
formed by thresholding the entries of the sample covariance matrix at $\rho$ is
decomposed into connected components. We show that the vertex-partition induced
by the thresholded covariance graph is exactly equal to that induced by the
estimated concentration graph. This simple rule, when used as a wrapper around
existing algorithms, leads to enormous performance gains. For large values of
$\rho$, our proposal splits a large graphical lasso problem into smaller
tractable problems, making it possible to solve an otherwise infeasible large
scale graphical lasso problem.

12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin