Optimal Transport and Ricci curvature: Difference between revisions
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Before we prove the Proposition, let us do some remarks: let <math> x \in M </math> be given and let <math> e_i </math> for <math> i = 1, \dots, n </math> be an orthonormal basis. After doing parallel transport along <math> \gamma</math>, we have an orthonormal basis also in <math> T_{T_t(x)}M</math>. Let <math> \textbf J</math> denote the matrix representation of <math> d_xT_t: T_xM\rightarrow T_{T_t(x)}M.</math> Then we have that | Before we prove the Proposition, let us do some remarks: let <math> x \in M </math> be given and let <math> e_i </math> for <math> i = 1, \dots, n </math> be an orthonormal basis. After doing parallel transport along <math> \gamma</math>, we have an orthonormal basis also in <math> T_{T_t(x)}M</math>. Let <math> \textbf J</math> denote the matrix representation of <math> d_xT_t: T_xM\rightarrow T_{T_t(x)}M.</math> Then we have that | ||
<math> J''+RJ=0</math> | <math> J''+RJ=0</math> | ||
where <math> R</math> denotes the matrix <math>(R(\gamma', e_i,\gamma', e_j))_{i,j}. | where <math> R</math> denotes the matrix <math>(R(\gamma', e_i,\gamma', e_j))_{i,j}.</math> | ||
Then we have that <math> J_i(t):= d_xT_t(e_i)</math> is a Jacobi field along <math> \gamma</math>. Now let | Then we have that <math> J_i(t):= d_xT_t(e_i)</math> is a Jacobi field along <math> \gamma</math>. Now let | ||
Revision as of 23:36, 14 February 2022
Introduction and Motivation
This article provides a brief introduction into a connection of optimal transport and the curvature tensor of a Riemannian manifold. In fact, we are going to study the transport map where denotes a vector field on the manifold
These kind of maps appear very naturally in the context of optimal transport. Recall that in optimal transport one is particularly interested in the Monge Problem, being the following optimization problem: Let be a compact and connected Riemannian manifold. Let furthermore, denote two probability measures on which are absolutely continuous with respect to the measure on the manifold, induced by the metric. the Monge Problem is then given by
where the infimum is taken among all measurable maps and denotes the Metric on induced by Then the Monge Problem admits a unique solution Moreover, in that case
for some (see [1]for more details of this).
To conclude the introductory part of this article, let us also mention that these kind of transport maps, turned out to be useful in the area of geometric analysis. In fact, Simon Brendle could prove a Sobolev inequality on non compact Riemannian manifolds with nonnegative Ricci curvature, the proof of which makes use of defining a map which is of the type ( see proof of Theorem 1.1 in [2] for more details).
Curvature and Optimal Transport
Let be a Riemannian manifold. In this article we assume basic knowledge about the notions of curvature and geodesics on a manifold. For some background information on these topics, we refer the reader to Chapter three to five in [3].
The Goal of this article is to show the follwing
Proposition
Let where denotes a vector field on and let Then the following inequality holds true:
where is defined to be the mapping which is a geodesic.
Notice that this inequality involves the transport map and the Ricci curvature tensor and therefore constitutes a connection of the curvature and the optimal transport problem.
Remarks
Before we prove the Proposition, let us do some remarks: let be given and let for be an orthonormal basis. After doing parallel transport along , we have an orthonormal basis also in . Let denote the matrix representation of Then we have that where denotes the matrix Then we have that is a Jacobi field along . Now let
References
- ↑ A. Figalli, C. Villiani, OPTIMAL TRANSPORT AND CURVATURE, Notes for a CIME lecture course in Cetraro, June 2009
- ↑ S. Brendle, Sobolev inequalities in manifolds with nonnegative curvature, 2021. arXiv: 2009.13717.
- ↑ M. P. do Carmo, Riemannian Geometry,Mathematics: Theory & Applications. Birkhauser Boston, Inc., Boston, MA, 1992