Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Communication-Avoiding Optimization Methods for Distributed
  Massive-Scale Sparse Inverse Covariance Estimation   Across a variety of scientific disciplines, sparse inverse covariance
estimation is a popular tool for capturing the underlying dependency
relationships in multivariate data. Unfortunately, most estimators are not
scalable enough to handle the sizes of modern high-dimensional data sets (often
on the order of terabytes), and assume Gaussian samples. To address these
deficiencies, we introduce HP-CONCORD, a highly scalable optimization method
for estimating a sparse inverse covariance matrix based on a regularized
pseudolikelihood framework, without assuming Gaussianity. Our parallel proximal
gradient method uses a novel communication-avoiding linear algebra algorithm
and runs across a multi-node cluster with up to 1k nodes (24k cores), achieving
parallel scalability on problems with up to ~819 billion parameters (1.28
million dimensions); even on a single node, HP-CONCORD demonstrates
scalability, outperforming a state-of-the-art method. We also use HP-CONCORD to
estimate the underlying dependency structure of the brain from fMRI data, and
use the result to identify functional regions automatically. The results show
good agreement with a clustering from the neuroscience literature.

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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: patt-sol
2
The categories of this paper are: math
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The categories of this paper are: nucl-ex