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A survey of the Schrödinger problem and some of its connections with optimal transport

Christian Léonard
- 01 Oct 2013 - 
- Vol. 34, Iss: 4, pp 1533-1574
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TLDR
In this paper, the authors present the Schrodinger problem and some of its connections with optimal transport, and give a user's guide to the problem and a survey of the related literature.
Abstract
This article is aimed at presenting the Schrodinger problem and some of its connections with optimal transport. We hope that it can be used as a basic user's guide to Schrodinger problem. We also give a survey of the related literature. In addition, some new results are proved.

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Citations
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Journal ArticleDOI

Convergence of Probability Measures

TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
Posted Content

Computational Optimal Transport

TL;DR: This short book reviews OT with a bias toward numerical methods and their applications in data sciences, and sheds lights on the theoretical properties of OT that make it particularly useful for some of these applications.
Journal ArticleDOI

Iterative Bregman Projections for Regularized Transportation Problems

TL;DR: In this article, a general numerical framework to approximate so-lutions to linear programs related to optimal transport is presented, where the set of linear constraints can be split in an intersection of a few simple constraints, for which the projections can be computed in closed form.
Posted Content

Iterative Bregman Projections for Regularized Transportation Problems

TL;DR: It is shown that for many problems related to optimal transport, the set of linear constraints can be split in an intersection of a few simple constraints, for which the projections can be computed in closed form.
References
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Book

Convergence of Probability Measures

TL;DR: Weak Convergence in Metric Spaces as discussed by the authors is one of the most common modes of convergence in metric spaces, and it can be seen as a form of weak convergence in metric space.
Book

Stochastic processes

J. L. Doob, +1 more
Book

Quantum Mechanics and Path Integrals

TL;DR: Au sommaire as discussed by the authors developed the concepts of quantum mechanics with special examples and developed the perturbation method in quantum mechanics and the variational method for probability problems in quantum physics.
Book

Continuous martingales and Brownian motion

Daniel Revuz, +1 more
TL;DR: In this article, the authors present a comprehensive survey of the literature on limit theorems in distribution in function spaces, including Girsanov's Theorem, Bessel Processes, and Ray-Knight Theorem.
Journal ArticleDOI

Convergence of Probability Measures

TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.