IPOT

Implementation of Inexact Proximal point method for Optimal Transport from paper "A Fast Proximal Point Method for Computing Exact Wasserstein Distance" (https://arxiv.org/abs/1802.04307).

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Package dependencies

It requires the following Python packages:

Python 3.5 numpy matplotlib pot 0.4.0 tensorflow 1.1.0

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Included modules

ipot.py: include function ipot_WD that computes the Wasserstein distance, a function ipot_barycenter that computes the Wasserstein barycenter.

ipot_demo.py: Demo for computing Wasserstein distance using ipot.ipot_WD.

learning_demo.py: 1D demo for learning generative model using ipot.ipot_WD. This file is the only one that needs Tensorflow.

barycenter_demo.py: Demo for computing Wasserstein barycenter.

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How to run the code

1. To compute Wasserstein distance:

python ipot_demo.py

2. To learn 1D generative model:

python learning_demo.py

3. To compute Wasserstein barycenter:

python barycenter_demo.py

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License

If you use my code, please cite our paper

@article{xie2018fast,
  title={A Fast Proximal Point Method for Computing Exact Wasserstein Distance},
  author={Xie, Yujia and Wang, Xiangfeng and Wang, Ruijia and Zha, Hongyuan},
  journal={arXiv preprint arXiv:1802.04307},
  year={2018}
}