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Questions tagged [optimal-transportation]

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Reweighting probability measures by convex potentials, and contraction in transport distance

Let $W: \mathbf{R}^d \to \mathbf{R}$ be a convex function such that $\int \exp(-W) = 1$, and define probability measures $\mu_y$ by $$\mu_y (dx) = \exp( - W (x - y)) \,dx,$$ i.e. each $\mu_y$ is a ...
πr8's user avatar
  • 801
1 vote
0 answers
130 views

Orthogonality in Wasserstein tangent space for discrete measures with equal mass

Let say I have $N$ discrete probability measures $(\mu_1,...,\mu_N)$ where each of them has $n$ points in $\mathbb{R}^2$ of equal mass. Let $P(\mathcal{X})$ be the space of these probability measures ...
Jean Hugue's user avatar
0 votes
1 answer
267 views

Intersection of projection of sets

Suppose that we have two arbitrary sets $\mathcal{X}$, $\mathcal{Y}$ and are given a function $c : \mathcal{X} \times \mathcal{Y} \rightarrow \mathbb{R}$ Consider the following inequality for ...
qp212223's user avatar
  • 143
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0 answers
291 views

Convexity of a set of probability densities

Consider the space of probability densities $(P(\mathbb{R}^d), W_2)$ (probability measures on $\mathbb{R}^d$ with 2-Wasserstein distance). How can we determine if a subset $Q$ is convex? I know that a ...
900edges's user avatar
  • 153
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0 answers
95 views

Empirical estimation of Brenier map from data

Let $f:\mathbb R^d \to \mathbb R$ be a "nice" (say, continuous) function define $A = A_f := \{x \in \mathbb R^d \mid f(x) \ge 0\}$ and $B =B_f:= \{x \in \mathbb R^d \mid f(x) \le 0\}$, and ...
dohmatob's user avatar
  • 6,853
0 votes
1 answer
86 views

Is integration against an indicator Wasserstein-Continuous

Let $\mathcal{P}_p(X)$ denote the Wasserstein space over a compact metric space $X$, and $1\leq p<\infty$. Fix a non-empty closed subset $C\subseteq X$. Then is the map: $$ \mathbb{P} \mapsto \...
ABIM's user avatar
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8 votes
1 answer
386 views

How does Otto theory work in this example of Wasserstein a.c. curve of probabilities?

I'm trying to read chapter 8 of the book on gradient flows by Ambrosio-Gigli-Savaré. In this context, I would like to better understand how the theory works for the following specific example. Take ...
Zatrapilla's user avatar
3 votes
2 answers
783 views

Relation between optimal transport cost and difference between topological invariants?

I was working on some mathematics of Wasserstein GAN and found out a seemingly interesting research problem but I am not quite sure whether it has already been studied in some recent literature of ...
Morino_Hikari's user avatar
2 votes
0 answers
123 views

Closure of finite support measures in the Wasserstein metric

This is a follow-up question to this question: Closure Wasserstein for pointmasses Let $(X,d)$ be a metric space, and let $W_1(X)$ be the space of probability measures $\mu$ on $X$ having finite first ...
Alain Valette's user avatar
4 votes
2 answers
225 views

Are there alternative regularizations for optimal transport problems besides entropic regularization?

I see that most of the regularization done involves an entropy term. Has there been any work done on other regularization methods? In particular, I'm wondering if anyone has done a regularization ...
Kashif's user avatar
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1 answer
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Wasserstein interpolation between two probability measures on a metric space

Question 1 Given probability measures $\mu$ and $\nu$ on the same metric space $X=(X,d)$, and $\alpha \in [0, 1]$, is it always possible to find another probability measure $\lambda_\alpha$ on $X$ ...
dohmatob's user avatar
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4 votes
1 answer
2k views

Is an ambiguity set with Wasserstein distance of order 1 is convex?

I have a question about the convexity of an Wasserstein ambiguity set. Let $W_1(\mu, \nu)$ be the Wasserstein distance of order 1 between $\mu$ and $\nu$, defined as $$W_1(\mu, \nu) := \min\limits_{\...
SYLee's user avatar
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1 vote
2 answers
889 views

Simplify Wasserstein distance between Gaussians with binary cost function

Let $\mu_1$ and $\mu_2$ be 1D gaussian distributions with means $m_1$ and $m_2$ respectively and common variance $\sigma$. Let $\Omega$ be a closed subset of $\mathbb R^2$, and consider the cost ...
dohmatob's user avatar
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2 votes
1 answer
228 views

Ideas on how to prove Pythagorean identity involving Wasserstein distances?

I conjectured earlier that if $P$ and $Q$ were two probability measures, then we could show $$W^2(P,Q) = \min_{T} [d^2(P,T_{\#}P) + W^2(T_{\#}P,Q)]$$ where $W^2(P,Q)$ denotes the squared Wasserstein-2 ...
Kashif's user avatar
  • 383
5 votes
2 answers
422 views

Bounding probability densities on a Wasserstein-2 geodesic

Consider two probability measures which are supported on a bounded domain $\Omega$ with density functions $p_0$ and $p_1$. It is well-known that for the Wasserstein-2 distance, there exists uniquely a ...
Elliott's user avatar
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1 vote
0 answers
37 views

Sufficient condition for an $n$-tuple to be a convex conjugate

We say $(f_1,f_2,\dotsc,f_N)$ is a convex conjugate if for any $i=1,2,\dotsc,N$ and any $x_i\in\Bbb R^d$, we have: $$f_i(x_i)=\sup\left\{\sum_{k=1}^{N}\sum_{j=k+1}^N x_k x_j - \sum_{j=1,j\neq i}^N f_j(...
Silentmovie's user avatar
1 vote
0 answers
85 views

Producing a minimiser for the Kantorovich problem from a minimiser of the Beckmann flow problem

Notation: We denote by $\mathcal M$ the set of vector valued measures on $\mathbb R^d$ whose divergence is a scalar measure (in the weak sense). Definitions: Consider the Beckmann flow minimisation ...
Nate River's user avatar
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5 votes
0 answers
140 views

What kinds of gradient-flows on $\mathbb R^d$ preserve the log-concavity of the distribution $\mu_0$ of starting point $x_0$

Let $\mu_0$ be a log-concave distribution on $\mathbb R^d$ and let $f:\mathbb R^d \to \mathbb R$ be $C^2$. Let $x_0$ be sampled uniformly at random from a log-concave distribution $\mu_0$, meaning ...
dohmatob's user avatar
  • 6,853
1 vote
1 answer
270 views

Wasserstein space with strictly non-positive sectional curvature

Let $(X,d)$ be a (separable, complete) metric space with uniformly strictly non-positive curvature in the sense of Alexandrov, i.e. $(X,d)$ satisfies a $CAT(K)$ inequality for some $K<0$. Does it ...
pseudocydonia's user avatar
4 votes
1 answer
430 views

Optimal transport: find cost function given observed transport

Could you advise me please on what to read on the "inverse" problem: suppose I have a source measure, a target measure and I observe the solution to optimal transport problem -- can I "back out" the ...
Sergey Egiev's user avatar
3 votes
2 answers
734 views

Maximum cost optimal transport

Kantorovich's optimal transportation problem \begin{equation} \tau_c(\mu,\nu)=\min\limits_{\pi\in\Pi(\nu,\mu)} \int_{X\times Y}c(x,y)d\pi(x,y) \end{equation} where $\Pi(\mu,\nu) = \{\pi\in P(X\times ...
Wen Ding's user avatar
1 vote
0 answers
113 views

Statistical analysis of optimization solution involving Brenier potentials?

I'm reading the paper https://arxiv.org/pdf/1905.10812.pdf where strongly convex approximations to Brenier potentials are approximated. Let $\mathcal{E}$ be a partition of $\mathbb{R}^{d}$ and $ 0\leq ...
Kashif's user avatar
  • 383
3 votes
0 answers
82 views

Semiconcavity estimate for the squared distance on a compact Riemannian manifold

I am currently reading this paper on the Riemannian structure of the Wasserstein space over a compact Riemannian manifold (my question doesn't concern the Wasserstein metric), specifically Section 4.1,...
grogTheFrog's user avatar
10 votes
1 answer
454 views

Open Questions about Wasserstein Space and PDE

While working on my thesis, I encountered the idea of OMT and started reading some more (like Villani's book). In particular, I came across a PhD thesis by Martial Agueh. I thought it was interesting ...
yoshi's user avatar
  • 427
4 votes
2 answers
415 views

Effect of perturbing the atoms of a measure on the Wasserstein distance

Let $(X,d)$ be a metric space, $x_1,\ldots,x_N\in X$ and $x_1',\ldots,x_N'\in X$ be atoms, and $G=\sum_{i=1}^Np_i\delta_{x_i}$, $G'=\sum_{i=1}^Np_i'\delta_{x_i}$, and $G''=\sum_{i=1}^Np_i'\delta_{x_i'}...
JohnA's user avatar
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8 votes
1 answer
623 views

Completion of spaces of measures w.r.t. weak norms

For the sake of concreteness denote by $M_0(X)$ the linear space of all signed Borel measures $\sigma$ with $\sigma(X)=0$ on some metric space $(X,d)$ and fix some base point $x_0\in X$. On this space ...
Dirk's user avatar
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6 votes
1 answer
580 views

Why is it difficult to solve the Monge problem directly?

I'm trying to understand something about the Monge problem. The Monge problem is: Let $c(x,y): \mathbb{R}^d \times \mathbb{R}^d \rightarrow \mathbb{R}^d$ and $$\mathcal{T}(\mu_1,\mu_2) = \{ T: \...
yoshi's user avatar
  • 427
1 vote
0 answers
96 views

Relationship between Wasserstein projections and metric projections in a linear space

Let $(X,d)$ be a metric space, $x\in X$, and $Y\subset X$ is a closed set. Assume that $X$ is also a real vector space, so that we can form linear combinations over $\mathbb{R}$, but I do not assume ...
user489304's user avatar
0 votes
0 answers
87 views

How does one define weak convergence of probability measures in $L^{\infty}(\Omega)$?

I am reading the following article and on page 9/17 (above Eqn (4.9)) the authors state that if $\gamma_{\epsilon_k}|\_G_{\delta}\times \Omega\to \gamma|\_G_{\delta}\times \Omega$ as $\epsilon_k\to 0$ ...
Student's user avatar
  • 547
2 votes
1 answer
289 views

On semi-discrete Wasserstein distance

Let $\mu(dx)=\sum_{i=1}^np_i\delta_{x_i}(dx)$ and $\nu(dy)=\rho(y)dy$ be two probability measures on $\mathbb R^d$, where $\nu$ has a bounded support. Consider the $2-$Wasserstein distance below: $$...
user111097's user avatar
1 vote
0 answers
543 views

Variational derivative of Wasserstein distance using Benaumou-Brenier formulation

I learned from the gradient flow theory in Wasserstein space that an equation of gradient flow type $$\partial_t \rho + \nabla \cdot (\rho \nabla \frac{\delta F}{\delta \rho})=0,$$ can be derived as ...
sam's user avatar
  • 429
0 votes
1 answer
141 views

Arbitrarily bad rates of convergence in Wasserstein metric

Suppose $W_p(\mu_n,\mu)\to 0$ and $d(E(\mu_n),E(\mu))<r_n$. Here, $W_p$ is the $p$th-order Wasserstein distance (with respect to the metric $d$) and $\mu_n,\mu$ are probability measures on some ...
user489304's user avatar
4 votes
0 answers
220 views

improved regularization for $\lambda$-convex gradient flows

It is well-known that gradient-flows of convex functionals are "parabolic" in some vague sense, and accordingly solutions tend to regularize instataneously. In the abstract context of gradient flows ...
leo monsaingeon's user avatar
2 votes
1 answer
571 views

Semi-discrete Wasserstein distance to uniform

Does the $p$-Wasserstein distance have a simpler expression when applied to these two distributions : A uniform distribution on $[0,1]^d$ A discrete distribution with $N$ equally-weighted point mass ...
lrnv's user avatar
  • 686
1 vote
1 answer
247 views

Scaling behavior of Wasserstein distances

Let $p>1$ and $\mu\neq \nu$ be two probability measures on $\Omega\subset \mathbb{R}^d$ a bounded set. For $\alpha \geq 0$, we let $$C_\alpha(\mu,\nu) = \inf_\sigma \frac{W_p(\mu+\sigma,\nu+\sigma)}...
Vincent's user avatar
  • 83
0 votes
0 answers
72 views

Optimal transport mapping between sets with a common subset

What would the optimal transport mapping between two sets with a common subset be? The problem I'm thinking about is the following: I'm in $\mathbb{C}^n$ and I have two distributions $\mu$ and $\nu$ ...
Kashif's user avatar
  • 383
3 votes
1 answer
142 views

Is the Wasserstein kernel positive definite?

Define a point cloud $X=\{x_i\}_{1\leq i\leq n}$, for $x_i\in\mathbb R^d$. Define the Wasserstein kernel as $$W(X,Y)=\max_{T}\frac{1}{n}\sum_{kl}T_{kl}\langle x_k,y_l \rangle$$ where $T$ is any doubly ...
tisydi's user avatar
  • 345
0 votes
1 answer
223 views

A problem with the dual form of semi-discrete optimal transport

Consider the uniform distribution $\lambda$ on $[0,1]$, and a point measure $\rho$ with density $\frac{1}{2} (\delta_{x_1} + \delta_{x_2})$, where we have $0\le x_1 \le x_2 < 1/2$. If our cost is ...
occassional user's user avatar
2 votes
0 answers
101 views

$CD(K,N)$ condition for non complete metric measure spaces

That's basically it. I would like to know if it's possible to define the ${\sf CD}(K,N)$ condition for metric measure spaces that are not necessarily complete. The references I have found on this ...
Mauricio Che's user avatar
5 votes
1 answer
396 views

Universal decay rate of the Fisher information along the heat flow

I'm looking for a reference for the following fact: In the torus $\mathbb T^d$ let me denote by $u_t=u(t,x)$ the (unique, distributional) solution of the heat equation $$ \partial_t u=\Delta u $$ ...
leo monsaingeon's user avatar
4 votes
1 answer
596 views

Monge-Kantorovich duality with a $\{0,1\}$ cost function

Consider the usual Monge-Kantorovich transportation problem where $X$ and $Y$ are Polish spaces, $\mu$ and $\nu$ are probability measures on $X$ and $Y$, and $c:X\times Y \to \mathbb{R}^+ \cup \{+\...
Tom Solberg's user avatar
  • 4,049
1 vote
1 answer
1k views

Monotonicity, Convexity, and Smoothness of the KL-Divergence between Two Brownian Motions with Different Initializers

We consider the two distributions $$ p_t = p_0 * N(0, tI),\quad q_t = q_0 * N(0, t I), $$ where $*$ denotes the convolution between two densities, while $p_0$ and $q_0$ have the same mean and ...
Minkov's user avatar
  • 1,127
3 votes
0 answers
163 views

A new "adversarial" Wasserstein distance?

Let us consider $\mu_1, \mu_2$ and $\mu_3$ three probability measures living on $[0,1]^{k_1}, [0,1]^{k_2}$ and $[0,1]^k$respectively, with $k_1 +k_2=k$. Let us denote by $\Gamma(\mu,\nu)$ the set of ...
Gilles Mordant's user avatar
8 votes
3 answers
936 views

Question about Wasserstein metric

Let $\mu$ and $\nu$ be two probability measures on $\mathbb R^n$ with finite first moment. Denote by $d:=W_1(\mu,\nu)$, where $W_1(\cdot,\cdot)$ stands for the Wasserstein distance of order $1$. My ...
user111097's user avatar
4 votes
0 answers
107 views

Upper bound $\tau_C := \int_{\|x\| \le 1}(vol(C \cap (x + C))/vol(C))dx$ for a convex body $C \subseteq \mathbb R^n$, by reducing to a ball

Let $C$ be a convex body in $\mathbb R^n$, i.e a bounded convex subset of $\mathbb R^n$ which has nonempty interior, and which is (A) open, or (B) closed (I'm not sure one makes more sense; choose the ...
dohmatob's user avatar
  • 6,853
3 votes
2 answers
165 views

continuity/ measurablity of optimal transport

given polish space $(X,d)$, consider weak* topology of probability. optimal transport of probability $u,v$ is defined by $\pi(u,v)$ such that $\pi(u,v)$ minimizes: $\{\int d(x,y) d \pi(dx,dy): \pi \...
jason's user avatar
  • 553
7 votes
2 answers
3k views

The largest Wasserstein distance to uniform distribution among all probability distributions with uniform marginals

I am looking for the largest Wasserstein distance to the uniform distribution among all probability distributions with uniform marginals. More specifically, let $\Xi=\{1,2,\ldots,N\}^2$, and let $\nu$...
O. Richard's user avatar
4 votes
1 answer
2k views

Earth movers distance (EMD) between two multivariate normals. Is it negative definite distance?

I was looking at the closed form formula for 2-Wassersteins distance for multivariate normal distribution on Wikipedia. https://en.wikipedia.org/wiki/Wasserstein_metric#Normal_distributions It has a ...
user24318's user avatar
  • 141
9 votes
3 answers
2k views

2-Wasserstein (optimal transport) and extension to the set of all signed measures

Consider the 2-Wasserstein distance between probability measures $\mu$ and $\nu$ (on $\mathbb{R}^d$), defined as $$ d_{W_2}(\mu,\nu) = \inf_{\gamma} \Big[\int \|x-y\|^2 d\gamma(x,y)\Big]^{1/2} $$ ...
passerby51's user avatar
  • 1,741
0 votes
1 answer
211 views

Relationship between a certain binary optimal transport and total-variation of modified distributions

Let $\mathcal X$ be a Polish space, and let $(N_x)_{x \in \mathcal X}$ be a system of closed neighborhoods in $\mathcal X$. Define $\Omega := \{(x,x') \in \mathcal X^2 \mid N_x \cap N_{x'} = \emptyset\...
dohmatob's user avatar
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