Optimal transport is a geometrically intuitive and robust way to quantify differences between probability measures.
It is becoming increasingly popular as numerical tool in image processing, computer vision and machine learning.
A key challenge is its efficient computation, in particular on large problems. Various algorithms exist, tailored to different special cases.
Multi-scale methods can be applied to classical discrete algorithms, as well as entropy regularization techniques. They provide a good compromise between efficiency and flexibility.