Math 2.0

Calculus of variations and optimal control; optimization Definition

Wasserstein metric

Builds on Metric spacePolish spaceProbability measureMarginal distributionLebesgue integralInfimumDirac measure

Definition

Let (M,d)(M, d) be a separable complete metric space, that is, a Polish space, and let p∈[1,∞)p \in [1, \infty). Let 𝒫p(M)\mathcal{P}_p(M) be the set of Borel probability measures μ\mu on MM with ∫Md(x,x0)p dμ(x)<∞\int_M d(x, x_0)^p \, d\mu(x) < \infty for some point x0∈Mx_0 \in M. For μ,ν∈𝒫p(M)\mu, \nu \in \mathcal{P}_p(M), a coupling of μ\mu and ν\nu is a Borel probability measure γ\gamma on M×MM \times M whose marginals are μ\mu and ν\nu. That is, γ(A×M)=μ(A)\gamma(A \times M) = \mu(A) and γ(M×A)=ν(A)\gamma(M \times A) = \nu(A) for every Borel set A⊆MA \subseteq M. Let Γ(μ,ν)\Gamma(\mu, \nu) be the set of couplings. The pp-Wasserstein distance is Wp(μ,ν)=(inf⁡γ∈Γ(μ,ν)∫M×Md(x,y)p dγ(x,y))1/p.W_p(\mu, \nu) = \left( \inf_{\gamma \in \Gamma(\mu, \nu)} \int_{M \times M} d(x, y)^p \, d\gamma(x, y) \right)^{1/p}.

Remarks

The condition on μ\mu does not depend on the choice of x0x_0, by the triangle inequality. The product measure μ⊗ν\mu \otimes \nu is always a coupling, so Γ(μ,ν)\Gamma(\mu, \nu) is not empty and Wp(μ,ν)<∞W_p(\mu, \nu) < \infty. The function WpW_p is a metric on 𝒫p(M)\mathcal{P}_p(M), and the infimum is attained.

Example: let δx\delta_x be the Dirac measure at xx. The only coupling of δx\delta_x and δy\delta_y is δ(x,y)\delta_{(x, y)}, so Wp(δx,δy)=d(x,y)W_p(\delta_x, \delta_y) = d(x, y).

Non-example: on M=ℝM = \R, the standard Cauchy distribution has ∫∣x∣ dμ=∞\int \abs{x} \, d\mu = \infty. So it does not lie in 𝒫1(ℝ)\mathcal{P}_1(\R), and W1W_1 is not defined for it.

On ℝ\R, with distribution functions FμF_\mu and FνF_\nu, one has W1(μ,ν)=∫−∞∞∣Fμ(t)−Fν(t)∣ dtW_1(\mu, \nu) = \int_{-\infty}^{\infty} \abs{F_\mu(t) - F_\nu(t)} \, dt. In 𝒫p(M)\mathcal{P}_p(M), Wp(μn,μ)→0W_p(\mu_n, \mu) \to 0 if and only if μn→μ\mu_n \to \mu weakly and the pp-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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Each page uses the pages above it. The colour of the bar on a box shows how strong the evidence is, and the box also gives it as text. The list has the same pages.
  • no evidence level
  1. Dirac measure concept
  2. Infimum concept
  3. Lebesgue integral concept
  4. Marginal distribution concept
  5. Metric space concept
  6. Polish space concept
  7. Probability measure concept
  8. Wasserstein metric definition

Further reading

  1. Web https://en.wikipedia.org/wiki/Wasserstein_metric
About this page
TagRC22S41ZDB
URIm2://RC22S41ZDB
Kinddefinition
WritingW0 stub
MSC49Q22, 60B10, 28A33
DefinesWasserstein metric
UsesMetric space, Polish space, Probability measure, Marginal distribution, Lebesgue integral, Infimum, Dirac measure
Changed2 November 2026

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