Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Accelerated Communication-Efficient Primal-Dual Optimization
  Framework for Structured Machine Learning   Distributed optimization algorithms are essential for training machine
learning models on very large-scale datasets. However, they often suffer from
communication bottlenecks. Confronting this issue, a communication-efficient
primal-dual coordinate ascent framework (CoCoA) and its improved variant CoCoA+
have been proposed, achieving a convergence rate of $\mathcal{O}(1/t)$ for
solving empirical risk minimization problems with Lipschitz continuous losses.
In this paper, an accelerated variant of CoCoA+ is proposed and shown to
possess a convergence rate of $\mathcal{O}(1/t^2)$ in terms of reducing
suboptimality. The analysis of this rate is also notable in that the
convergence rate bounds involve constants that, except in extreme cases, are
significantly reduced compared to those previously provided for CoCoA+. The
results of numerical experiments are provided to show that acceleration can
lead to significant performance gains.

16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ