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This numerical tours details how to solve the discrete optimal transport problem (in the case of measures that are sums of Diracs) using linear programming.
from __future__ import division
import nt_toolbox as nt
from nt_solutions import optimaltransp_1_linprog as solutions
%matplotlib inline
%load_ext autoreload
%autoreload 2
We consider two dicretes distributions $$ \forall k=0,1, \quad \mu_k = \sum_{i=1}^{n_k} p_{k,i} \de_{x_{k,i}} $$ where $n_0,n_1$ are the number of points, $\de_x$ is the Dirac at location $x \in \RR^d$, and $ X_k = ( x_{k,i} )_{i=1}^{n_i} \subset \RR^d$ for $k=0,1$ are two point clouds.
We define the set of couplings between $\mu_0,\mu_1$ as $$ \Pp = \enscond{ (\ga_{i,j})_{i,j} \in (\RR^+)^{n_0 \times n_1} }{ \forall i, \sum_j \ga_{i,j} = p_{0,i}, \: \forall j, \sum_i \ga_{i,j} = p_{1,j} } $$
The Kantorovitch formulation of the optimal transport reads $$ \ga^\star \in \uargmin{\ga \in \Pp} \sum_{i,j} \ga_{i,j} C_{i,j} $$ where $C_{i,j} \geq 0$ is the cost of moving some mass from $x_{0,i}$ to $x_{1,j}$.
The optimal coupling $\ga^\star$ can be shown to be a sparse matrix with less than $n_0+n_1-1$ non zero entries. An entry $\ga_{i,j}^\star \neq 0$ should be understood as a link between $x_{0,i}$ and $x_{1,j}$ where an amount of mass equal to $\ga_{i,j}^\star$ is transfered.
In the following, we concentrate on the $L^2$ Wasserstein distance. $$ C_{i,j}=\norm{x_{0,i}-x_{1,j}}^2. $$
The $L^2$ Wasserstein distance is then defined as $$ W_2(\mu_0,\mu_1)^2 = \sum_{i,j} \ga_{i,j}^\star C_{i,j}. $$
The coupling constraint $$ \forall i, \sum_j \ga_{i,j} = p_{0,i}, \: \forall j, \sum_i \ga_{i,j} = p_{1,j} $$ can be expressed in matrix form as $$ \Sigma(n_0,n_1) \ga = [p_0;p_1] $$ where $ \Sigma(n_0,n_1) \in \RR^{ (n_0+n_1) \times (n_0 n_1) } $.
flat = lambda x: x(: )
Cols = lambda n0, n1: sparse(flat(repmat(1: n1, [n0 1])), ...
flat(reshape(1: n0*n1, n0, n1)), ...
ones(n0*n1, 1))
Rows = lambda n0, n1: sparse(flat(repmat(1: n0, [n1 1])), ...
flat(reshape(1: n0*n1, n0, n1)'), ...
ones(n0*n1, 1))
Sigma = lambda n0, n1: [Rows(n0, n1); Cols(n0, n1)]
We use a simplex algorithm to compute the optimal transport coupling $\ga^\star$.
maxit = 1e4; tol = 1e-9
otransp = lambda C, p0, p1: reshape(perform_linprog(...
Sigma(length(p0), length(p1)), ...
[p0(: ); p1(: )], C(: ), 0, maxit, tol), [length(p0) length(p1)])
Dimensions $n_0, n_1$ of the clouds.
n0 = 60
n1 = 80
Compute a first point cloud $X_0$ that is Gaussian. and a second point cloud $X_1$ that is Gaussian mixture.
gauss = lambda q, a, c: a*randn(2, q) + repmat(c(: ), [1 q])
X0 = randn(2, n0)*.3
X1 = [gauss(n1/ 2, .5, [0 1.6]) gauss(n1/ 4, .3, [-1 -1]) gauss(n1/ 4, .3, [1 -1])]
Density weights $p_0, p_1$.
normalize = lambda a: a/ sum(a(: ))
p0 = normalize(rand(n0, 1))
p1 = normalize(rand(n1, 1))
Shortcut for display.
myplot = lambda x, y, ms, col: plot(x, y, 'o', 'MarkerSize', ms, 'MarkerEdgeColor', 'k', 'MarkerFaceColor', col, 'LineWidth', 2)
Display the point clouds. The size of each dot is proportional to its probability density weight.
clf; hold on
for i in 1: length(p0):
myplot(X0(1, i), X0(2, i), p0(i)*length(p0)*10, 'b')
for i in 1: length(p1):
myplot(X1(1, i), X1(2, i), p1(i)*length(p1)*10, 'r')
axis([min(X1(1, : )) max(X1(1, : )) min(X1(2, : )) max(X1(2, : ))]); axis off
Compute the weight matrix $ (C_{i,j})_{i,j}. $
C = repmat(sum(X0.^2)', [1 n1]) + ...
repmat(sum(X1.^2), [n0 1]) - 2*X0'*X1
Compute the optimal transport plan.
gamma = otransp(C, p0, p1)
Check that the number of non-zero entries in $\ga^\star$ is $n_0+n_1-1$.
fprintf('Number of non-zero: %d (n0 + n1-1 = %d)\n', full(sum(gamma(: )~ = 0)), n0 + n1-1)
Check that the solution satifies the constraints $\ga \in \Cc$.
fprintf('Constraints deviation (should be 0): %.2e, %.2e.\n', norm(sum(gamma, 2)-p0(: )), norm(sum(gamma, 1)'-p1(: )))
For any $t \in [0,1]$, one can define a distribution $\mu_t$ such that $t \mapsto \mu_t$ defines a geodesic for the Wasserstein metric.
Since the $W_2$ distance is a geodesic distance, this geodesic path solves the following variational problem $$ \mu_t = \uargmin{\mu} (1-t)W_2(\mu_0,\mu)^2 + t W_2(\mu_1,\mu)^2. $$ This can be understood as a generalization of the usual Euclidean barycenter to barycenter of distribution. Indeed, in the case that $\mu_k = \de_{x_k}$, one has $\mu_t=\de_{x_t}$ where $ x_t = (1-t)x_0+t x_1 $.
Once the optimal coupling $\ga^\star$ has been computed, the interpolated distribution is obtained as $$ \mu_t = \sum_{i,j} \ga^\star_{i,j} \de_{(1-t)x_{0,i} + t x_{1,j}}. $$
Find the $i,j$ with non-zero $\ga_{i,j}^\star$.
[I, J, gammaij] = find(gamma)
Display the evolution of $\mu_t$ for a varying value of $t \in [0,1]$.
tlist = linspace(0, 1, 6)
for i in 1: length(tlist):
t = tlist(i)
Xt = (1-t)*X0(: , I) + t*X1(: , J)
subplot(2, 3, i)
hold on
for i in 1: length(gammaij):
myplot(Xt(1, i), Xt(2, i), gammaij(i)*length(gammaij)*6, [t 0 1-t])
title(['t = ' num2str(t, 2)])
axis([min(X1(1, : )) max(X1(1, : )) min(X1(2, : )) max(X1(2, : ))]); axis off
In the case where the weights $p_{0,i}=1/n, p_{1,i}=1/n$ (where $n_0=n_1=n$) are constants, one can show that the optimal transport coupling is actually a permutation matrix. This properties comes from the fact that the extremal point of the polytope $\Cc$ are permutation matrices.
This means that there exists an optimal permutation $ \si^\star \in \Sigma_n $ such that $$ \ga^\star_{i,j} = \choice{ 1 \qifq j=\si^\star(i), \\ 0 \quad\text{otherwise}. } $$ where $\Si_n$ is the set of permutation (bijections) of $\{1,\ldots,n\}$.
This permutation thus solves the so-called optimal assignement problem $$ \si^\star \in \uargmin{\si \in \Sigma_n} \sum_{i} C_{i,\si(j)}. $$
Same number of points.
n0 = 40
n1 = n0
Compute points clouds.
X0 = randn(2, n0)*.3
X1 = [gauss(n1/ 2, .5, [0 1.6]) gauss(n1/ 4, .3, [-1 -1]) gauss(n1/ 4, .3, [1 -1])]
Constant distributions.
p0 = ones(n0, 1)/ n0
p1 = ones(n1, 1)/ n1
Compute the weight matrix $ (C_{i,j})_{i,j}. $
C = repmat(sum(X0.^2)', [1 n1]) + ...
repmat(sum(X1.^2), [n0 1]) - 2*X0'*X1
Display the coulds.
clf; hold on
myplot(X0(1, : ), X0(2, : ), 10, 'b')
myplot(X1(1, : ), X1(2, : ), 10, 'r')
axis equal; axis off
Solve the optimal transport.
gamma = otransp(C, p0, p1)
Show that $\ga$ is a binary permutation matrix.
imageplot(gamma)
Display the optimal assignement.
clf; hold on
[I, J, ~] = find(gamma)
for k in 1: length(I):
h = plot([X0(1, I(k)) X1(1, J(k))], [X0(2, I(k)) X1(2, J(k))], 'k')
set(h, 'LineWidth', 2)
myplot(X0(1, : ), X0(2, : ), 10, 'b')
myplot(X1(1, : ), X1(2, : ), 10, 'r')
axis equal; axis off