  The Stochastic Gradient Descent method (SGD) and its stochastic variants have
become methods of choice for solving finite-sum optimization problems arising
from machine learning and data science thanks to their ability to handle
large-scale applications and big datasets. In the last decades, researchers
have made substantial effort to study the theoretical performance of SGD and
its shuffling variants. However, only limited work has investigated its
shuffling momentum variants, including shuffling heavy-ball momentum schemes
for non-convex problems and Nesterov's momentum for convex settings. In this
work, we extend the analysis of the shuffling momentum gradient method
developed in [Tran et al (2021)] to both finite-sum convex and strongly convex
optimization problems. We provide the first analysis of shuffling
momentum-based methods for the strongly convex setting, attaining a convergence
rate of $O(1/nT^2)$, where $n$ is the number of samples and $T$ is the number
of training epochs. Our analysis is a state-of-the-art, matching the best rates
of existing shuffling stochastic gradient algorithms in the literature.
