Shuffling Gradient Descent-Ascent with Variance Reduction for
Nonconvex-Strongly Concave Smooth Minimax Problems
Shuffling Gradient Descent-Ascent with Variance Reduction for
Nonconvex-Strongly Concave Smooth Minimax Problems
In recent years, there has been considerable interest in designing stochastic first-order algorithms to tackle finite-sum smooth minimax problems. To obtain the gradient estimates, one typically relies on the uniform sampling-with-replacement scheme or various sampling-without-replacement (also known as shuffling) schemes. While the former is easier to analyze, the latter often …