Fiches de cours

Optimal transport

English

Remarque

(pas donné en 2018-19)

Summary

The first part is devoted to Monge and Kantorovitch problems, discussing the existence and the properties of the optimal plan. The second part introduces the Wasserstein distance on measures and develops applications of optimal transport to PDEs, functional/geometric inequalities, traffic models.

Content

The theory of optimal transport began in the eighteenth century with the Monge problem (1781), which is to minimize the cost of transporting an amount of material from the given set of origins to the given set of destinations. In the fourties, Kantorovitch gave an important reformulation of the problem and, since then, the Monge-Kantorovitch problem has been a classical subject in probability theory, economics and optimization. More recently, the interplay between optimal transport and various fields such as PDEs (Ricci flow, Euler equations¿), fluid mechanics, geometric analysis (isoperimetric and Sobolev inequalities, curvature-dimension conditions), functional analysis, urban planning and economics has been deeply investigated.

The first part of the course will be devoted to Monge and Kantorovitch's problems, discussing the existence and the properties of the optimal plan under different conditions on the cost. We will exploit the relation with Kantorovitch's duality theorem, with Brenier's polar decomposition theorem, and with the Monge-Ampere equation, a PDE which arises naturally in this context. The second part of the course will be centered on the applications of optimal transport to different problems: after introducing the Wasserstein distance, we will see the connection with some PDEs, with functional/geometric inequalities, as well as the application to traffic models.

Learning Prerequisites

Recommended courses

Basic background in analysis (calculus, measure theory and metric spaces); a few concepts of functional analysis (briefly reviewed along the course).

Assessment methods

Oral

From the rulebook of the Section of Mathematics (art. 3 al. 5), the teacher decides of the form of the exam and communicates it to the concerned students.

Dans les plans d'études

• Ingénierie mathématique, 2018-2019, Master semestre 2
• Semestre
Printemps
• Forme de l'examen
Oral
• Crédits
5
• Matière examinée
Optimal transport
• Cours
2 Heure(s) hebdo x 14 semaines
• Exercices
2 Heure(s) hebdo x 14 semaines
• Ingénierie mathématique, 2018-2019, Master semestre 4
• Semestre
Printemps
• Forme de l'examen
Oral
• Crédits
5
• Matière examinée
Optimal transport
• Cours
2 Heure(s) hebdo x 14 semaines
• Exercices
2 Heure(s) hebdo x 14 semaines
• Mathématiques - master, 2018-2019, Master semestre 2
• Semestre
Printemps
• Forme de l'examen
Oral
• Crédits
5
• Matière examinée
Optimal transport
• Cours
2 Heure(s) hebdo x 14 semaines
• Exercices
2 Heure(s) hebdo x 14 semaines
• Mathématiques pour l'enseignement, 2018-2019, Master semestre 2
• Semestre
Printemps
• Forme de l'examen
Oral
• Crédits
5
• Matière examinée
Optimal transport
• Cours
2 Heure(s) hebdo x 14 semaines
• Exercices
2 Heure(s) hebdo x 14 semaines
• Mathématiques pour l'enseignement, 2018-2019, Master semestre 4
• Semestre
Printemps
• Forme de l'examen
Oral
• Crédits
5
• Matière examinée
Optimal transport
• Cours
2 Heure(s) hebdo x 14 semaines
• Exercices
2 Heure(s) hebdo x 14 semaines

LuMaMeJeVe
8-9
9-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
19-20
20-21
21-22
Cours
Exercice, TP
Projet, autre

légende

• Semestre d'automne
• Session d'hiver
• Semestre de printemps
• Session d'été
• Cours en français
• Cours en anglais
• Cours en allemand