@Article{AAM-38-2,
author = {Liu, Hailiang and Xuping, Tian},
title = {An Adaptive Gradient Method with Energy and Momentum},
journal = {Annals of Applied Mathematics},
year = {2022},
volume = {38},
number = {2},
pages = {183--222},
abstract = {<p style="text-align: justify;">We introduce a novel algorithm for gradient-based optimization of
stochastic objective functions. The method may be seen as a variant of SGD
with momentum equipped with an adaptive learning rate automatically adjusted
by an ‘energy’ variable. The method is simple to implement, computationally
efficient, and well suited for large-scale machine learning problems. The method
exhibits unconditional energy stability for any size of the base learning rate. We
provide a regret bound on the convergence rate under the online convex optimization framework. We also establish the energy-dependent convergence rate
of the algorithm to a stationary point in the stochastic non-convex setting. In
addition, a sufficient condition is provided to guarantee a positive lower threshold for the energy variable. Our experiments demonstrate that the algorithm
converges fast while generalizing better than or as well as SGD with momentum
in training deep neural networks, and compares also favorably to Adam.</p>},
issn = {},
doi = {https://doi.org/10.4208/aam.OA-2021-0095},
url = {https://global-sci.com/article/72631/an-adaptive-gradient-method-with-energy-and-momentum}
}