Optimal Transport Wiki:Asymptotic equivalence of W 2 and H^-1

expository stuff to be filled in

Motivation

When the quadratic Wasserstein and ${\dot {H}}^{-1}$ distance become asymptotically equivalent when the involved densities are close to $\varrho =1$. This is particularly of interest in evolution problems with a constraint of $\varrho =1$ such as crowd motion.

Formalization

Lemma

Let $\mu ,\nu$ be absolutely continuous measures on a convex domain $\Omega$, with densities bounded by the same constant $C>0$. Then, for all functions $\phi \in H^{1}(\Omega )$:

$\int _{\Omega }\phi \,\mathrm {d} (\mu -\nu )\leq {\sqrt {C}}||\nabla \phi ||_{L^{2}(\Omega )}W_{2}(\mu ,\nu )$

Proof of the lemma can be found Chapter 5, page 210 of .

Definition of ${\dot {H}}^{-1}$

The negative Sobolev norm ${\dot {H}}^{-1}$ is defined to be

$||\mu -\nu ||_{{\dot {H}}^{-1}(\Omega )}={\text{sup}}\left\{\int _{\Omega }\phi \,\mathrm {d} (\mu -\nu ):\phi \in C^{1}(\Omega ),\,||\nabla \phi ||_{L^{2}}\leq 1\right\}.$

Theorem

Let $\mu ,\nu$ be absolutely continuous measures on a convex domain $\Omega$, with densities bounded from below and from above by the same constants $a,b$ with $0<a<b<+\infty$. Then

$b^{-{\frac {1}{2}}}||\mu -\nu ||_{{\dot {H}}^{-1}(\Omega )}\leq W_{2}(\mu ,\nu )\leq a^{-{\frac {1}{2}}}||\mu -\nu ||_{{\dot {H}}^{-1}(\Omega )}$

The proof of the theorem uses the above lemma and can be found Chapter 5, page 211 of .

Application to Localization

The following material is adapted from .

This section deals with the problem of localization of the quadratic Wasserstein distance: if $\mu ,\nu$ are measures on $\mathbb {R} ^{d}$ that are close in the sense of $W_{2}$, do they remain close to each other when restricted to subsets of $\mathbb {R} ^{d}$?

Notation

Here we are working in Euclidean space $\mathbb {R} ^{d}$ whose norm will be denoted by $|\cdot |$. When necessary, the Lebesgue measure on $\mathbb {R} ^{d}$ will be denoted by $\lambda$.

• Recall that for a subset $A\subseteq \mathbb {R} ^{d}$,

$\mathrm {dist} (x,A):=\inf\{|x-y|:y\in A\}$

denotes the distance between a point $x$ and the subset $A$.

• For a measure $\mu$ on $\mathbb {R} ^{d}$ and $\varphi :\mathbb {R} ^{d}\to \mathbb {R}$ a nonnegative and measurable function, $\varphi \cdot \mu$ denotes the measure such that $\mathrm {d} (\varphi \cdot \mu )=\varphi (x)\,\mathrm {d} \mu (x)$.
• The norm

$\|\mu \|_{1}:=\int _{\mathbb {R} ^{d}}\,|\mathrm {d} \mu (x)|$

denotes the total variation norm of the measure $\mu$.

• For $\mu$ a measure supported on $A\subseteq \mathbb {R} ^{d}$, define the norm

$\|\mu \|_{L^{2}(A)}:=\left[\int _{A}\left({\frac {\mathrm {d} \mu }{\mathrm {d} \lambda }}(x)\right)^{2}\,\mathrm {d} \lambda (x)\right]^{\frac {1}{2}}.$

Theorem

Let $\mu ,\nu$ be mesures on $\mathbb {R} ^{d}$ having the same total mass, and let $B=B_{R}(a)$ be a ball in $\mathbb {R} ^{d}$. Assume that on $B$, the density of $\mu$ with respect to the Lebesgue measure is bounded above and below, that is

$\exists 0<m_{1}\leq m_{2}<\infty \quad \forall x\in B\quad m_{1}\mathrm {d} x\leq \mathrm {d} \mu (x)\leq m_{2}\mathrm {d} x.$

Let $\varphi :\mathbb {R} ^{d}\to (0,+\infty )$ be a $k$-Lipschitz function for some $0\leq k<\infty$ supported in $B$, and suppose that $\varphi$ is bounded above and below by the map $x\mapsto \mathrm {dist} (x,B^{c})^{2}$ on $B$, that is, there exists constants $0<c_{1}\leq c_{2}<\infty$ such that for all $x\in B$,

$c_{1}\mathrm {dist} (x,B^{c})^{2}\leq \varphi (x)\leq c_{2}\mathrm {dist} (x,B^{c})^{2}.$

Then, denoting $a:=\|\varphi \cdot \nu \|_{1}/\|\varphi \cdot \mu \|_{1}$,

$W_{2}(a\varphi \cdot \mu ,\varphi \cdot \nu )\leq C(n)^{\frac {1}{2}}\left({\frac {c_{2}m_{2}}{c_{1}m_{1}}}\right)^{\frac {3}{2}}kc_{1}^{-{\frac {1}{2}}}W_{2}(\mu ,\nu ),$

for $C(n)<\infty$ some absolute constant depending only on $n$. Moreover, taking $C(n):=2^{11}n$ fits.