Skip to yearly menu bar Skip to main content


Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function

Lingkai Kong · Molei Tao

Poster Session 1 #463

Keywords: [ Efficient Inference Methods ] [ Deep Learning ] [ Algorithms ] [ Boosting and Ensemble Methods ]


This article suggests that deterministic Gradient Descent, which does not use any stochastic gradient approximation, can still exhibit stochastic behaviors. In particular, it shows that if the objective function exhibit multiscale behaviors, then in a large learning rate regime which only resolves the macroscopic but not the microscopic details of the objective, the deterministic GD dynamics can become chaotic and convergent not to a local minimizer but to a statistical distribution. In this sense, deterministic GD resembles stochastic GD even though no stochasticity is injected. A sufficient condition is also established for approximating this long-time statistical limit by a rescaled Gibbs distribution, which for example allows escapes from local minima to be quantified. Both theoretical and numerical demonstrations are provided, and the theoretical part relies on the construction of a stochastic map that uses bounded noise (as opposed to Gaussian noise).

Chat is not available.