Discriminative Bayesian Filtering Lends Momentum to the Stochastic Newton Method for Minimizing Log-Convex Functions

To minimize the average of a set of log-convex functions, the stochastic Newton method iteratively updates its estimate using subsampled versions of the full objective's gradient and Hessian. We contextualize this optimization problem as sequential Bayesian inference on a latent state-space model with a discriminatively-specified observation process. Applying Bayesian filtering then yields a novel optimization algorithm that considers the entire history of gradients and Hessians when forming an update. We establish matrix-based conditions under which the effect of older observations diminishes over time, in a manner analogous to Polyak's heavy ball momentum. We illustrate various aspects of our approach with an example and review other relevant innovations for the stochastic Newton method.

This code reproduces the figures found in this manuscript. It can be run on Python 3.10.2 using the provided Dockerfile (https://www.docker.com):

docker build -t hibiscus .
docker run --rm -ti -v $(pwd):/home/felixity hibiscus

or in a virtual environment with Python3.10 (https://docs.python.org/3.10/):

python3.10 -m venv turquoise
source turquoise/bin/activate
pip3 install -r requirements.txt
python3.10 filtered_stochastic_newton.py