Wasserstein metric

Definition (Wasserstein metric)

Let (X,d)(\mathcal{X},d) be a Polish metric space, and let 1p<1\le p<\infty. For any two Probability Measures μ,ν\mu,\nu on X\mathcal{X} the Wasserstein Metric of order pp between μ\mu and ν\nu is defined by the formula Wp(μ,ν)=(infπΠ(μ,ν)X×Xd(x,y)pdπ(x,y))1/p=inf{[E[d(X,Y)p]1/p,law(X)=μ,law(Y)=ν}\begin{align*} W_{p}(\mu,\nu)&=\left( \inf_{\pi \in\Pi(\mu,\nu)}\int\limits _{\mathcal{X}\times \mathcal{X}}d(x,y)^{p} \, d\pi(x,y) \right)^{1/p}\\ &=\inf\left \{ [E_{}\left[ d(X,Y)^{p} \right]^{1/p},\,\text{law}(X)=\mu,\,\text{law}(Y)=\nu \right\} \end{align*}

Definition (Wasserstein-1)

The Wasserstein metric of order 1 for any two Measures μ,νP(X)\mu,\nu \in\mathcal{P}(\mathbb{X}) is defined as W1(μ,ν)=infηH(μ,ν)X×Xη(dx,dy)xyW_{1}(\mu,\nu)=\inf_{\eta \in\mathcal{H}(\mu,\nu)}\int\limits _{\mathbb{X}\times \mathbb{X}}\eta(dx,dy)|x-y| where H(μ,ν)\mathcal{H}(\mu,\nu) denotes the set of probability measures on X×X\mathbb{X}\times \mathbb{X} with the first marginal μ\mu and second marginal ν\nu. Furthermore we equivalently have W1(μ,ν)=supfLip1fdμfdνW_{1}(\mu,\nu)=\sup_{\lVert f \rVert _{Lip}\le 1}\left|\int\limits f \, d\mu -\int\limits f \, d\nu \right|where fLip:=supxyf(x)f(y)dX(x,y)\lVert f \rVert _{Lip}:=\sup_{x\not=y} \frac{f(x)-f(y)}{d_{\mathbb{X}}(x,y)}and dXd_{\mathbb{X}} is the metric on X\mathbb{X}.

Definition (Wasserstein convergence)

Let (μn)nNP(X),μP(X)(\mu_{n})_{n\in\mathbb{N}}\subset \mathcal{P}(\mathbb{X}),\mu \in\mathcal{P}(\mathbb{X}). We say that μnμ\mu_{n}\to \mu in W1(μn,μ)W_{1}(\mu_{n},\mu) (the Wasserstein metric) if W1(μn,μ)0 as nW_{1}(\mu_{n},\mu)\to0\text{ as }n\to \infty

Definition (Wasserstein space)

With the same convention defined in the Wasserstein metric the Wasserstein space of order pp is defined as Pp(X):={μP(X)|Xd(x0,x)pμ(dx)<+}x0XP_{p}(\mathcal{X}):=\left\{ \mu \in\mathcal{P}(\mathcal{X})\middle| \int\limits _{\mathcal{X}}d(x_{0},x)^{p} \, \mu(dx)<+\infty \right \}\quad x_{0}\in\mathcal{X}where x0Xx_{0}\in\mathcal{X} is arbitrary. This space does not depend on the choice of the point x0x_{0}. Then WpW_{p} defines a finite Metric on Pp(X)P_{p}(\mathcal{X}).

The Wasserstein space is the space of Probability Measures which have a finite Moment of order pp.

Theorem (Wasserstein distance metrizes Wasserstein space (Villani))

Let (X,d)(\mathcal{X},d) be a Polish space, and 1p<1\le p<\infty; then the Wasserstein metric WpW_{p} metrizes the weak convergence in Pp(X)P_{p}(\mathcal{X}). i.e. if (μk)kNPp(X)(\mu_{k})_{k\in\mathbb{N}}\subset P_{p}(\mathcal{X}) and μPp(X)\mu \in P_{p}(\mathcal{X}), then the statements μk converges weakly in Pp(X)toμ\mu_{k} \text{ converges weakly in } P_{p}(\mathcal{X})\,to\,\muand Wp(μk,μ)0W_{p}(\mu_{k},\mu)\to0are equivalent.

Definition (Weak convergence in Wasserstein space)

Let (X,d)(\mathcal{X},d) be a Polish space, and 1p<1\le p<\infty. Let (μk)kNPp(X)(\mu_{k})_{k\in\mathbb{N}}\subset P_{p}(\mathcal{X}) and μPp(X)\mu \in P_{p}(\mathcal{X}) where Pp(X)P_{p}(\mathcal{X}) is the . Then μk\mu_{k} is said to converge weakly in Pp(X)P_{p}(\mathcal{X}) if any one of the following properties is satisfied for any x0Xx_{0}\in\mathcal{X}:

  1. μkμ\mu_{k}\to \mu and d(x0,x)pdμk(x)d(x0,x)pdμ(x)\int\limits d(x_{0},x)^{p} \, d\mu_{k}(x)\to \int\limits d(x_{0},x)^{p} \, d\mu(x)
  2. μkμ\mu_{k}\to \mu and lim supkd(x0,x)pdμk(x)d(x0,x)pdμ(x)\limsup_{ k \to \infty } \int\limits d(x_{0},x)^{p} \, d\mu_{k}(x)\le \int\limits d(x_{0},x)^{p} \, d\mu(x)
  3. μkμ\mu_{k}\to \mu and limRlim supkd(x0,x)Rd(x0,x)pdμk(x)=0\lim_{ R \to \infty } \limsup_{ k \to \infty } \int\limits_{d(x_{0},x)\ge R} d(x_{0},x)^{p} \, d\mu_{k}(x)=0
  4. For all continuous functions φ\varphi with φ(x)C(1+d(x0,x)p)|\varphi(x)|\le C(1+d(x_{0},x)^{p}), CRC\in\mathbb{R}, one has φ(x)dμk(x)φ(x)dμ(x)\int\limits \varphi(x) \, d\mu_{k}(x)\to \int\limits \varphi(x) \, d\mu(x)

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