1 code implementation • 3 Aug 2022 • Hailiang Liu, Xuping Tian
In this paper, we propose SGEM, Stochastic Gradient with Energy and Momentum, to solve a large class of general non-convex stochastic optimization problems, based on the AEGD method that originated in the work [AEGD: Adaptive Gradient Descent with Energy.
1 code implementation • 23 Mar 2022 • Hailiang Liu, Xuping Tian
We introduce a novel algorithm for gradient-based optimization of stochastic objective functions.
no code implementations • 29 Sep 2021 • Hailiang Liu, Xuping Tian
In this paper, we propose SGDEM, Stochastic Gradient Descent with Energy and Momentum to solve a large class of general nonconvex stochastic optimization problems, based on the AEGD method that originated in the work [AEGD: Adaptive Gradient Descent with Energy.
1 code implementation • 10 Oct 2020 • Hailiang Liu, Xuping Tian
We propose AEGD, a new algorithm for first-order gradient-based optimization of non-convex objective functions, based on a dynamically updated energy variable.