Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Distributed Coordinate Descent for Non-Strongly Convex Losses   We propose an efficient distributed randomized coordinate descent method for
minimizing regularized non-strongly convex loss functions. The method attains
the optimal $O(1/k^2)$ convergence rate, where $k$ is the iteration counter.
The core of the work is the theoretical study of stepsize parameters. We have
implemented the method on Archer - the largest supercomputer in the UK - and
show that the method is capable of solving a (synthetic) LASSO optimization
problem with 50 billion variables.

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The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys