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Tuning hyperparameters of Hamiltonian Monte Carlo

This is the code used to do the experiments of the article A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization.

Installation instructions

2D toy examples

These experiments were done with numpy, scipy, and autograd, so these packages have to be installed.

Variational Autoencoder

The framework TensorFlow 1.15 was used here. For installation instruction, see https://www.tensorflow.org/install.

1D experiments and molecular configuration sampling

We used the framework PyTorch 1.6 for these experiments, see https://pytorch.org/get-started/locally/ for installation instructions. The experiments involving alanine dipeptide require OpenMM to be installed, which can be done via conda. The other dependencies can be installed via

pip install -r requirements.txt

The scripts for running the experiments are in the molecular-configurations directory. Each experiment can be reproduced using the respective configuration file in molecular-configurations/config.