Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling   Accelerated coordinate descent is widely used in optimization due to its
cheap per-iteration cost and scalability to large-scale problems. Up to a
primal-dual transformation, it is also the same as accelerated stochastic
gradient descent that is one of the central methods used in machine learning.
  In this paper, we improve the best known running time of accelerated
coordinate descent by a factor up to $\sqrt{n}$. Our improvement is based on a
clean, novel non-uniform sampling that selects each coordinate with a
probability proportional to the square root of its smoothness parameter. Our
proof technique also deviates from the classical estimation sequence technique
used in prior work. Our speed-up applies to important problems such as
empirical risk minimization and solving linear systems, both in theory and in
practice.

14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
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The categories of this paper are: eess
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The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg