Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Alternating proximal-gradient steps for (stochastic) nonconvex-concave
  minimax problems   Minimax problems of the form $\min_x \max_y \Psi(x,y)$ have attracted
increased interest largely due to advances in machine learning, in particular
generative adversarial networks. These are typically trained using variants of
stochastic gradient descent for the two players.
  Although convex-concave problems are well understood with many efficient
solution methods to choose from, theoretical guarantees outside of this setting
are sometimes lacking even for the simplest algorithms.
  In particular, this is the case for alternating gradient descent ascent,
where the two agents take turns updating their strategies.
  To partially close this gap in the literature we prove a novel global
convergence rate for the stochastic version of this method for finding a
critical point of $g(\cdot) := \max_y \Psi(\cdot,y)$ in a setting which is not
convex-concave.

12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con