Computational Optimal Transport - Exercise Notebooks

This is a collection of Jupyter / Python notebooks for the computational optimal transport course at the Mathematical Sciences Institute, Australian National University.

The notebooks are best viewed online using nbviewer as GitHub doesn't do a great job at renderring the LaTeX / MathJax in Jupyter notebooks. Click on the links below to view the notebooks in nbviewer:

• Part 1a: Linear programming

  https://nbviewer.org/github/james-nichols/COT_notebooks/blob/main/COT_1a_linear_programming.ipynb

• Part 1b: Monotone rearrangement

  https://nbviewer.org/github/james-nichols/COT_notebooks/blob/main/COT_1b_monotone_rearrangement.ipynb

• Part 2a: Network simplex method

  https://nbviewer.org/github/james-nichols/COT_notebooks/blob/main/COT_2a_network_simplex.ipynb

• Part 2b: Entropic regularisation

  https://nbviewer.org/github/james-nichols/COT_notebooks/blob/main/COT_2b_entropic_regularisation.ipynb

To complete and run these notebooks you will need a recent installation of Python (Version 3.6+) along with the Numpy, Scipy, and matplotlib packages. You will also need to install Jupyter.