  In several experimental reports on nonconvex optimization problems in machine
learning, stochastic gradient descent (SGD) was observed to prefer minimizers
with flat basins in comparison to more deterministic methods, yet there is very
little rigorous understanding of this phenomenon. In fact, the lack of such
work has led to an unverified, but widely-accepted stochastic mechanism
describing why SGD prefers flatter minimizers to sharper minimizers. However,
as we demonstrate, the stochastic mechanism fails to explain this phenomenon.
Here, we propose an alternative deterministic mechanism that can accurately
explain why SGD prefers flatter minimizers to sharper minimizers. We derive
this mechanism based on a detailed analysis of a generic stochastic quadratic
problem, which generalizes known results for classical gradient descent.
Finally, we verify the predictions of our deterministic mechanism on two
nonconvex problems.
