  In the context of stochastic gradient descent(SGD) and adaptive moment
estimation (Adam),researchers have recently proposed optimization techniques
that transition from Adam to SGD with the goal of improving both convergence
and generalization performance. However, precisely how each approach trades off
early progress and generalization is not well understood; thus, it is unclear
when or even if, one should transition from one approach to the other. In this
work, by first studying the convex setting, we identify potential contributors
to observed differences in performance between SGD and Adam. In particular,we
provide theoretical insights for when and why Adam outperforms SGD and vice
versa. We ad-dress the performance gap by adapting a single global learning
rate for SGD, which we refer to as AdaSGD. We justify this proposed approach
with empirical analyses in non-convex settings. On several datasets that span
three different domains,we demonstrate how AdaSGD combines the benefits of both
SGD and Adam, eliminating the need for approaches that transition from Adam to
SGD.
