Formal Riemannian Structure of the Wasserstein metric: Difference between revisions

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Given a closed and convex space <math> X \subseteq R^d </math>, two probability measures on the same space, <math> \mu, \nu \in \mathcal{P}_2(X) </math>, the Wasserstein metric is defined as  
Given a closed and convex space <math> X \subseteq R^d </math>, two probability measures on the same space, <math> \mu, \nu \in \mathcal{P}_2(X) </math>, the 2-Wasserstein metric is defined as  


:<math> W_2(\mu, \nu) := \min_{\gamma \in \Gamma(\mu, \nu)} \left( \int |x_1 - x_2|^2 \, d\gamma(x_1, x_2) \right)^{1/2}  </math>
:<math> W_2(\mu, \nu) := \min_{\gamma \in \Gamma(\mu, \nu)} \left( \int |x_1 - x_2|^2 \, d\gamma(x_1, x_2) \right)^{1/2}  </math>


where <math> \Gamma(\mu, \nu) </math> is a transport plan from <math> \mu </math> to <math> \nu </math>. These plans are used to define the [[Kantorovich Problem]]. The Wasserstein metric is indeed a metric in the sense that it satisfies the desired properties of a distance function between probability measures on <math> \mathcal{P}_2(X)</math>. Moreover, the Wasserstein metric can be used to define a Riemannian metric on <math> \mathcal{P}_2(X) </math>. Such a metric allows one to define angles and lengths of vectors at each point in our ambient space. This structure can then be used to apply tools and phenomena found in Riemannian geometry, such as [[Geodesics and generalized geodesics|geodesics]].
where <math> \Gamma(\mu, \nu) </math> is the set of [[Kantorovich Problem|transport plans]] from <math> \mu </math> to <math> \nu </math>. The Wasserstein metric is indeed a metric in the sense that it satisfies the desired properties of a distance function between probability measures on <math> \mathcal{P}_2(X)</math>. Moreover, the Wasserstein metric can be used to define a formal Riemannian metric on <math> \mathcal{P}_2(X) </math>. Such a formal metric structure allows one to define angles and lengths of vectors at each point in our ambient space.


==Basic Structure of Riemannian Manifolds==
==Tangent Space Induced by the Wasserstein Metric==
A convenient way to formalize tangent vectors in this setting is to consider time derivatives of curves on the manifold. A tangent vector at a point <math> \rho </math> would be the time derivative at 0 of a curve, <math> \rho(t) </math>, where <math> \rho(0) = \rho </math><ref name="Villani1"/>. Since we are dealing with a space of probability measures, additional restrictions need to be added in order to make our tangent space well-defined. For example, we would like our trajectory to satisfy the continuity equation <math> \frac{\partial \rho}{\partial t} + \nabla \cdot (\rho v) = 0 </math>. There are many such vector fields that solve the continuity equation, so we can restrict to a vector field that minimizes kinetic energy, which is defined as <math> \int \rho|v|^2 </math>. This choice of tangent vectors is justified by the following lemma
 
:'''Lemma'''<ref name="Ambrosio, Gigli, Savaré"/> A vector <math> v \in L^2(\rho; X) </math> belongs to the tangent cone at <math> \rho </math> iff
:<math> \lVert v + w \rVert \ge \lVert v \rVert \; \forall w \in L^2(\rho; X) \; \mbox{such that} \; \nabla \cdot (w\rho) = 0 </math>
 
where we are taking the <math> L^2(\rho, X) </math> norm. Divergence condition implies that our tangent vectors are equivalent up to a vector field with zero divergence. This implies that <math> v </math> is in fact a gradient of some function <math> u </math>, in which case our continuity equation becomes


==Tangent Space Induced by the Wasserstein Metric==
:<math> \frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \nabla u) = 0 </math><ref name="Villani1"/>
 
This is an elliptic partial differential equation, so one can apply tools used for that class of PDEs in order to determine existence and uniqueness of the tangent vectors.


==Riemannian Metric Induced by the Wasserstein Metric==
==Riemannian Metric Induced by the Wasserstein Metric==
Given two tangent vectors at a point <math> \rho </math> in our space, <math> \mathcal{P}_2(X) </math>, we can define the Riemannian metric as follows
:<math> \left\langle \frac{\partial \rho}{\partial t_1}, \frac{\partial \rho}{\partial t_2} \right\rangle_\rho = \int \rho \langle \nabla u_1, \nabla u_2 \rangle </math><ref name="Villani2" />
Here, <math> \frac{\partial \rho}{\partial t_1}, \frac{\partial \rho}{\partial t_2} </math> are tangent vectors at <math> \rho </math>, and <math> u_1, u_2 </math> are solutions to the modified continuity equation from the previous section. This metric defines an inner product at every point in our space, <math> \mathcal{P}_2(X) </math>. This not only allows one to define [[Geodesics and generalized geodesics|geodesics]] in this space, but the metric can be used to define calculus operators such as gradients and Hessians. These operators can be applied to in a similar manner to the same operators in finite dimensional Riemannian manifolds.


==References==
==References==
<references>
<references>
<ref name="Ambrosio, Gigli, Savaré ">[https://link.springer.com/content/pdf/10.1007%2Fb137080.pdf L. Ambrosio, N. Gigli,G. Savaré, ''Gradient Flows in Metric Spaces and in the Space of Probability Measures'', p. 189-191]</ref>
<ref name="Ambrosio, Gigli, Savaré ">[https://link.springer.com/content/pdf/10.1007%2Fb137080.pdf L. Ambrosio, N. Gigli,G. Savaré, ''Gradient Flows in Metric Spaces and in the Space of Probability Measures'', p. 189-191]</ref>
<ref name="Villani">[https://people.math.gatech.edu/~gangbo/Cedric-Villani.pdf C. Villani, ''Topics in Optimal Transportation'', p. 245-247, 250-251]</ref>
<ref name="Villani1">[https://people.math.gatech.edu/~gangbo/Cedric-Villani.pdf C. Villani, ''Topics in Optimal Transportation'', p. 245-247]</ref>
<ref name="Villani2">[https://people.math.gatech.edu/~gangbo/Cedric-Villani.pdf C. Villani, ''Topics in Optimal Transportation'', p. 250-251]</ref>
</references>
</references>

Latest revision as of 04:37, 28 February 2022

Given a closed and convex space , two probability measures on the same space, , the 2-Wasserstein metric is defined as

where is the set of transport plans from to . The Wasserstein metric is indeed a metric in the sense that it satisfies the desired properties of a distance function between probability measures on . Moreover, the Wasserstein metric can be used to define a formal Riemannian metric on . Such a formal metric structure allows one to define angles and lengths of vectors at each point in our ambient space.

Tangent Space Induced by the Wasserstein Metric

A convenient way to formalize tangent vectors in this setting is to consider time derivatives of curves on the manifold. A tangent vector at a point would be the time derivative at 0 of a curve, , where [1]. Since we are dealing with a space of probability measures, additional restrictions need to be added in order to make our tangent space well-defined. For example, we would like our trajectory to satisfy the continuity equation . There are many such vector fields that solve the continuity equation, so we can restrict to a vector field that minimizes kinetic energy, which is defined as . This choice of tangent vectors is justified by the following lemma

Lemma[2] A vector belongs to the tangent cone at iff

where we are taking the norm. Divergence condition implies that our tangent vectors are equivalent up to a vector field with zero divergence. This implies that is in fact a gradient of some function , in which case our continuity equation becomes

[1]

This is an elliptic partial differential equation, so one can apply tools used for that class of PDEs in order to determine existence and uniqueness of the tangent vectors.

Riemannian Metric Induced by the Wasserstein Metric

Given two tangent vectors at a point in our space, , we can define the Riemannian metric as follows

[3]

Here, are tangent vectors at , and are solutions to the modified continuity equation from the previous section. This metric defines an inner product at every point in our space, . This not only allows one to define geodesics in this space, but the metric can be used to define calculus operators such as gradients and Hessians. These operators can be applied to in a similar manner to the same operators in finite dimensional Riemannian manifolds.

References