Implementations of the algorithms in the paper Approximative Algorithms for Multi-Marginal Optimal Transport and Free-Support Wasserstein Barycenters

Last update: Oct 29, 2022

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Overview

MOT

Implementations of the algorithms in the paper Approximative Algorithms for Multi-Marginal Optimal Transport and Free-Support Wasserstein Barycenters (von Lindheim, 2022). Using the emd OT solver from the Python Optimal Transport (POT) package, which is a wrapper of this network simplex solver, which, in turn, is based on an implementation in the LEMON C++ library.

Installation

Download the code or clone the Github repository with

git clone https://github.com/jvlindheim/mot.git

For the code in mot.py, there is the following dependencies: numpy, matplotlib.pyplot, the cdist function from scipy.spatial.distance and the emd function from the POT library. You can install them e.g. using pip via

pip install --user numpy scipy matplotlib POT

If you want to run the demo notebook, you will also need to have Jupyter Notebook or JupyterLab installed.

Owner

Johannes von Lindheim

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