# A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization

arXiv: https://arxiv.org/abs/1802.05155 · HTML: https://mlsanity.com/paper/1802.05155/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2018.

## Built on by (most cited first, 2 of 2)

- [SHANG++: Robust Stochastic Acceleration under Multiplicative Noise](https://mlsanity.com/paper/2603.09355/) (2026)
- [Accelerate Distributed Stochastic Descent for Nonconvex Optimization with Momentum](https://mlsanity.com/paper/2110.00625/) (2021)

## Abstract (arXiv)

Momentum Stochastic Gradient Descent (MSGD) algorithm has been widely applied to many nonconvex optimization problems in machine learning, e.g., training deep neural networks, variational Bayesian inference, and etc. Despite its empirical success, there is still a lack of theoretical understanding of convergence properties of MSGD. To fill this gap, we propose to analyze the algorithmic behavior of MSGD by diffusion approximations for nonconvex optimization problems with strict saddle points and isolated local optima. Our study shows that the momentum helps escape from saddle points, but hurts the convergence within the neighborhood of optima (if without the step size annealing or momentum annealing). Our theoretical discovery partially corroborates the empirical success of MSGD in training deep neural networks.