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Optimal Transport, Numerics and Sampling

MA5933Elective Modules3 ECTSEnglishUnregelmäßigDepartment Mathematics
AI-edited module sheet. Based on the TUMonline module description, edited for readability.Original in TUMonline

What it is about

In this module you will learn the theory of optimal transport and methods for its numerical treatment. You will understand the fundamental models (Monge, Kantorovich), important properties of Wasserstein spaces and gain an overview of numerical procedures and applications in Data Science.

What you will be able to do

  • Understanding of Monge and Kantorovich formulations
  • Knowledge of duality, Brenier theorem and cyclic monotonicity
  • Familiarity with Wasserstein distances and geodesics in the W_p space
  • Insight into numerical methods: Benamou–Brenier, semidiscrete algorithms, Sinkhorn
  • Application perspective for sampling, quantization and data-science problems

What the module consists of

  • VorlesungPresentation of the content, demonstrative examples and discussion with students

Teaching method

  • Online-Vorlesung via ZoomLecture with examples and discussion; aims to encourage independent deepening and literature work
  • Slides and virtual boardVisualization of content and derivations during the lecture
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Official page in TUMonline · Details are not binding.