Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic Variance Reduction Methods for Saddle-Point Problems   We consider convex-concave saddle-point problems where the objective
functions may be split in many components, and extend recent stochastic
variance reduction methods (such as SVRG or SAGA) to provide the first
large-scale linearly convergent algorithms for this class of problems which is
common in machine learning. While the algorithmic extension is straightforward,
it comes with challenges and opportunities: (a) the convex minimization
analysis does not apply and we use the notion of monotone operators to prove
convergence, showing in particular that the same algorithm applies to a larger
class of problems, such as variational inequalities, (b) there are two notions
of splits, in terms of functions, or in terms of partial derivatives, (c) the
split does need to be done with convex-concave terms, (d) non-uniform sampling
is key to an efficient algorithm, both in theory and practice, and (e) these
incremental algorithms can be easily accelerated using a simple extension of
the "catalyst" framework, leading to an algorithm which is always superior to
accelerated batch algorithms.

8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph