Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Convergent and Dimension-Independent First-Order Algorithm for Min-Max
  Optimization   Motivated by the recent work of Mangoubi and Vishnoi (STOC 2021), we propose
a variant of the min-max optimization framework where the max-player is
constrained to update the maximization variable in a greedy manner until it
reaches a *first-order* stationary point. We present an algorithm that provably
converges to an approximate local equilibrium for our framework from any
initialization and for nonconvex-nonconcave loss functions. Compared to the
second-order algorithm of Mangoubi and Vishnoi, whose iteration bound is
polynomial in the dimension, our algorithm is first-order and its iteration
bound is independent of dimension. We empirically evaluate our algorithm on
challenging nonconvex-nonconcave test-functions and loss functions that arise
in GAN training. Our algorithm converges on these test functions and, when used
to train GANs on synthetic and real-world datasets, trains stably and avoids
mode collapse.

35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: eess
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ