SGHMC Algorithm Implementation

Qinzhe Wang (qw92@duke.edu)

Yi Mi (yi.mi@duke.edu)

Introduction

This project implemented the Stochastic Gradient Hamiltonian Monte Carlo algorithm. Numba, C++ and Cholesky Decomposition were utilized to optimize the performance of the code. The algorithm was applied on simulated dataset and tested on a handwritten digits classification task using the MNIST dataset, compared to SGLD and SGD with momentum methods. The package was created and published on TestPyPI.

Structure

application: Applications code on simulated and real data

figures: Reproducible figures

optimization: Optimization code using Numba, C++, and Cholesky Decomposition

report: Report and original paper

sghmc: Source code of SGHMC algorithm

test: Test algorithm and package

Installation

This package is published on TestPyPI.

pip install -i https://test.pypi.org/simple/ sghmc-2021

Usage.

import sghmc
sghmc.sghmc()

Maintainers

Qinzhe Wang (qw92@duke.edu)

Yi Mi (yi.mi@duke.edu)