Calculus of variations and optimal control; optimization Definition
Wasserstein metric
Builds on Metric spacePolish spaceProbability measureMarginal distributionLebesgue integralInfimumDirac measure
Definition
Let be a separable complete metric space, that is, a Polish space, and let . Let be the set of Borel probability measures on with for some point . For , a coupling of and is a Borel probability measure on whose marginals are and . That is, and for every Borel set . Let be the set of couplings. The -Wasserstein distance is
Remarks
The condition on does not depend on the choice of , by the triangle inequality. The product measure is always a coupling, so is not empty and . The function is a metric on , and the infimum is attained.
Example: let be the Dirac measure at . The only coupling of and is , so .
Non-example: on , the standard Cauchy distribution has . So it does not lie in , and is not defined for it.
On , with distribution functions and , one has . In , if and only if weakly and the -th moments converge.
Reading path
To understand this page from the ground up, read these 8 pages in order. The foundations come first.
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- no evidence level
- Dirac measure concept
- Infimum concept
- Lebesgue integral concept
- Marginal distribution concept
- Metric space concept
- Polish space concept
- Probability measure concept
- Wasserstein metric definition
Further reading
About this page
| Tag | RC22S41ZDB |
|---|---|
| URI | m2://RC22S41ZDB |
| Kind | definition |
| Writing | W0 stub |
| MSC | 49Q22, 60B10, 28A33 |
| Defines | Wasserstein metric |
| Uses | Metric space, Polish space, Probability measure, Marginal distribution, Lebesgue integral, Infimum, Dirac measure |
| Changed | 2 November 2026 |
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