  We introduce a new analytic framework to analyze the convergence of the
Randomized Stochastic Gradient Descent Ascent (RSGDA) algorithm for stochastic
minimax optimization problems. Under the so-called NC-PL condition on one of
the variables, our analysis improves the state-of-the-art convergence results
in the current literature and hence broadens the applicable range of the RSGDA.
We also introduce a simple yet effective strategy to accelerate RSGDA , and
empirically validate its efficiency on both synthetic data and real data.
