  In this paper, we propose a unified convergence analysis for a class of
generic shuffling-type gradient methods for solving finite-sum optimization
problems. Our analysis works with any sampling without replacement strategy and
covers many known variants such as randomized reshuffling, deterministic or
randomized single permutation, and cyclic and incremental gradient schemes. We
focus on two different settings: strongly convex and nonconvex problems, but
also discuss the non-strongly convex case. Our main contribution consists of
new non-asymptotic and asymptotic convergence rates for a wide class of
shuffling-type gradient methods in both nonconvex and convex settings. We also
study uniformly randomized shuffling variants with different learning rates and
model assumptions. While our rate in the nonconvex case is new and
significantly improved over existing works under standard assumptions, the rate
on the strongly convex one matches the existing best-known rates prior to this
paper up to a constant factor without imposing a bounded gradient condition.
Finally, we empirically illustrate our theoretical results via two numerical
examples: nonconvex logistic regression and neural network training examples.
As byproducts, our results suggest some appropriate choices for diminishing
learning rates in certain shuffling variants.
