An accelerated first-order regularized momentum descent ascent algorithm
  for stochastic nonconvex-concave minimax problems
Huiling Zhang Zi Xu
Stochastic nonconvex minimax problems have attracted wide attention in
machine learning, signal processing and many other fields in recent years. In
this paper, we propose an accelerated first-order regularized momentum descent
ascent algorithm (FORMDA) for solving stochastic nonconvex-concave minimax
problems. The iteration complexity of the algorithm is proved to be
$\tilde{\mathcal{O}}(\varepsilon ^{-6.5})$ to obtain an
$\varepsilon$-stationary point, which achieves the best-known complexity bound
for single-loop algorithms to solve the stochastic nonconvex-concave minimax
problems under the stationarity of the objective function.