  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.
