# sinkhorn: Wasserstein Distance by Entropic Regularization In T4transport: Tools for Computational Optimal Transport

 sinkhorn R Documentation

## Wasserstein Distance by Entropic Regularization

### Description

Due to high computational cost for linear programming approaches to compute Wasserstein distance, \insertCitecuturi_sinkhorn_2013;textualT4transport proposed an entropic regularization scheme as an efficient approximation to the original problem. This comes with a regularization parameter \lambda > 0 in the term

\lambda h(\Gamma) = \lambda \sum_{m,n} \Gamma_{m,n} \log (\Gamma_{m,n}).

As \lambda\rightarrow 0, the solution to an approximation problem approaches to the solution of a true problem. However, we have an issue with numerical underflow. Our implementation returns an error when it happens, so please use a larger number when necessary.

### Usage

sinkhorn(X, Y, p = 2, wx = NULL, wy = NULL, lambda = 0.1, ...)

sinkhornD(D, p = 2, wx = NULL, wy = NULL, lambda = 0.1, ...)


### Arguments

 X an (M\times P) matrix of row observations. Y an (N\times P) matrix of row observations. p an exponent for the order of the distance (default: 2). wx a length-M marginal density that sums to 1. If NULL (default), uniform weight is set. wy a length-N marginal density that sums to 1. If NULL (default), uniform weight is set. lambda a regularization parameter (default: 0.1). ... extra parameters including maxitermaximum number of iterations (default: 496). abstolstopping criterion for iterations (default: 1e-10). D an (M\times N) distance matrix d(x_m, y_n) between two sets of observations.

### Value

a named list containing

distance

\mathcal{W}_p distance value.

iteration

the number of iterations it took to converge.

plan

an (M\times N) nonnegative matrix for the optimal transport plan.

\insertAllCited

### Examples


#-------------------------------------------------------------------
#  Wasserstein Distance between Samples from Two Bivariate Normal
#
# * class 1 : samples from Gaussian with mean=(-1, -1)
# * class 2 : samples from Gaussian with mean=(+1, +1)
#-------------------------------------------------------------------
## SMALL EXAMPLE
set.seed(100)
m = 20
n = 10
X = matrix(rnorm(m*2, mean=-1),ncol=2) # m obs. for X
Y = matrix(rnorm(n*2, mean=+1),ncol=2) # n obs. for Y

## COMPARE WITH WASSERSTEIN
outw = wasserstein(X, Y)
skh1 = sinkhorn(X, Y, lambda=0.05)
skh2 = sinkhorn(X, Y, lambda=0.10)

## VISUALIZE : SHOW THE PLAN AND DISTANCE
pm1 = paste0("wasserstein plan ; distance=",round(outw$distance,2)) pm2 = paste0("sinkhorn lbd=0.05; distance=",round(skh1$distance,2))
pm5 = paste0("sinkhorn lbd=0.1 ; distance=",round(skh2$distance,2)) opar <- par(no.readonly=TRUE) par(mfrow=c(1,3)) image(outw$plan, axes=FALSE, main=pm1)
image(skh1$plan, axes=FALSE, main=pm2) image(skh2$plan, axes=FALSE, main=pm5)
par(opar)



T4transport documentation built on April 12, 2023, 12:37 p.m.