Teaching: Computational optimal transport (lecture+exercises)

Overview

Target audience / what to expect?

This lecture will provide a basic introduction to optimal transport with a focus on the computational/algorithmic side.

Everyone should have a good intuitive grasp on basic mathematics such as linear algebra and (finite-dimensional) analysis. We will encounter concepts like Lagrange multipliers, optimization and algorithmic tools such as Dijkstra's shortest path algorithm. The lecture will contain detailed proofs but these do not play a major role in the exam. The exams will most likely be held orally.

We will closely follow the lecture material with computational examples. For this we will rely mostly on Python. All examples can be run on the GWDG jupyter cloud.

Tentative list of topics

Literature