Formal Riemannian Structure of the Wasserstein metric: Difference between revisions
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where <math> \Gamma(\mu, \nu) </math> is a [[Kantorovich Problem|transport plan]] 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 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]], to the field of optimal transport. | where <math> \Gamma(\mu, \nu) </math> is a [[Kantorovich Problem|transport plan]] 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 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]], to the field of optimal transport. | ||
==Tangent Space Induced by the Wasserstein Metric== | ==Tangent Space Induced by the Wasserstein Metric== |
Revision as of 06:51, 6 June 2020
Given a closed and convex space , two probability measures on the same space, , the 2-Wasserstein metric is defined as
where is a transport plan 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 Riemannian metric on . 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, to the field of optimal transport.
Tangent Space Induced by the Wasserstein Metric
Riemannian Metric Induced by the Wasserstein Metric
References
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