Optimal Transport: Fast Probabilistic Approximation with Exact Solvers

Optimal Transport: Fast Probabilistic Approximation with Exact Solvers

Max Sommerfeld   ,  Jörn Schrieber11footnotemark: 1   , Axel Munk33footnotemark: 3 Supported by the DFG Research Training Group 2088 “Discovering Structure in Complex Data: Statistics Meets Optimization and Inverse Problems”.Felix-Bernstein Institute for Mathematical Statistics in the Biosciences, University Göttingen, Goldschmidtstr. 7, 37077 GöttingenInstitute for Mathematical Stochastics, University Göttingen, Goldschmidtstr. 7, 37077 GöttingenMax-Planck-Institute for Biophysical Chemistry, Am Faßberg 11, 37077 Göttingen
Email: max.sommerfeld@mathematik.uni-goettingen.de, joern.schrieber-1@mathematik.uni-goettingen.de, munk@math.uni-goettingen.de

We propose a simple subsampling scheme for fast randomized approximate computation of optimal transport distances. This scheme operates on a random subset of the full data and can use any exact algorithm as a black-box back-end, including state-of-the-art solvers and entropically penalized versions. It is based on averaging the exact distances between empirical measures generated from independent samples from the original measures and can easily be tuned towards higher accuracy or shorter computation times. To this end, we give non-asymptotic deviation bounds for its accuracy. In particular, we show that in many important cases, including images, the approximation error is independent of the size of the full problem. We present numerical experiments that demonstrate that a very good approximation in typical applications can be obtained in a computation time that is several orders of magnitude smaller than what is required for exact computation of the full problem.

1 Introduction

Optimal transport distances, a.k.a. Wasserstein, earth-mover’s, Monge-Kantorovich-Rubinstein or Mallows distances, as metrics to compare probability measures (Rachev and Rüschendorf, 1998; Villani, 2008) have become a popular tool in a wide range of applications in computer science, machine learning and statistics. Important examples are image retrieval (Rubner et al., 2000) and classification (Zhang et al., 2007), computer vision (Ni et al., 2009), but also therapeutic equivalence (Munk and Czado, 1998), generative modeling (Bousquet et al., 2017), biometrics (Sommerfeld and Munk, 2016), metagenomics (Evans and Matsen, 2012) and medical imaging (Ruttenberg et al., 2013).

Optimal transport distances compare probability measures by incorporating a ground distance on the underlying space. This often makes if preferable to competing distances such as total-variation or \chi^{2}-distances, which are oblivious to any metric or similarity structure on the ground space. In this setting, it has a clear and intuitive interpretation as the amount of ’work’ required to transport one probability distribution onto the other. This notion is typically well-aligned with human perception of similarity (Rubner et al., 2000).

Figure 1: Relative error and relative runtime compared to the exact computation of the proposed scheme. Optimal transport distances and its approximations were computed between images of different sizes (32\times 32, 64\times 64, 128\times 128). Each point represents a specific parameter choice in the scheme and is a mean over different problem instances, solvers and cost exponents. For the relative runtimes the geometric mean is reported. For details on the parameters see Figure 2.

1.1 Computation

The outstanding theoretical and practical performance of optimal transport distances is contrasted by its excessive computational cost. For example, optimal transport distances can be computed with an auction algorithm (Bertsekas, 1992). For two probability measures supported on N points this algorithm has a worst case run time of \mathcal{O}(N^{3}\log N). Other methods like the transportation simplex have sub-cubic empirical average runtime (compare Gottschlich and Schuhmacher (2014)), but exponential worst case runtimes.

Many attempts have therefore been made to design improved algorithms. We give some selective references: Ling and Okada (2007) proposed a specialized algorithm for L_{1}-ground distance and \mathcal{X} a regular grid and report an empirical runtime of \mathcal{O}(N^{2}). Gottschlich and Schuhmacher (2014) improved existing general purpose algorithms by initializing with a greedy heuristic. Their Shortlist algorithm achieves an empirical average runtime of the order \mathcal{O}(N^{5/2}). Schmitzer (2016) solves the optimal transport problem by solving a sequence of sparse problems.

Despite these efforts, many practically relevant problems remain well outside the scope of available algorithms. See (Schrieber et al., 2017) for an overview and a numerical comparison of state-of-the-art algorithms for discrete optimal transport. This is true in particular for two or three dimensional images and spatio temporal imaging, which constitute an important area of potential applications. Here, N is the number of pixels or voxels and is typically of size 10^{5} to 10^{7}. Naturally, this problem is aggravated when many distances have to be computed as is the case for Wasserstein barycenters (Agueh and Carlier, 2011; Cuturi and Doucet, 2014), which have become an important use case.

To bypass the computational bottleneck, many surrogates for optimal transport distances that are more amenable to fast computation have been proposed. Shirdhonkar and Jacobs (2008) proposed to use an equivalent distance based on wavelets that can be computed in linear time but cannot be calibrated to approximate the Wasserstein distance with arbitrary accuracy. Pele and Werman (2009) threshold the ground distance to reduce the complexity of the underlying linear program, obtaining a lower bound for the exact distance. Cuturi (2013) altered the optimization problem by adding an entropic penalty term in order to use faster and more stable algorithms, see also (Altschuler et al., 2017). Bonneel et al. (2015) consider the 1-D Wasserstein distances of radial projections of the original measures, exploiting the fact that, in one dimension, computing the Wasserstein distance amounts to sorting the point masses and hence has quasi-linear computation time.

1.2 Contribution

We do not propose a new algorithm to solve the optimal transport problem. Instead, we propose a simple probabilistic scheme as a meta-algorithm that can use any algorithm (e.g. those mentioned above) as a black-box back-end and gives a random but fast approximation of the exact distance. This scheme

  1. is extremely easy to implement, to parallelize and to tune towards higher accuracy or shorter computation time as desired;

  2. can be used with any algorithm for transportation problems as a back-end, including general LP solvers, specialized network solvers and algorithms using entropic penalization (Cuturi, 2013);

  3. comes with theoretical non-asymptotic guarantees for the approximation error - in particular, this error is independent of the size of the original problem in many important cases, including images;

  4. works well in practice. For example, the Wasserstein distance between two 128^{2}-pixel images can typically be approximated with a relative error of less than 5\% in only 1\% of the time required for exact computation.

2 Problem and Algorithm

Although our meta-algorithm is applicable to exact solvers for any optimal transport distance between probability measures, for example the Sinkhorn distance (Cuturi, 2013), the theory we present here concerns the Kantorovich transport distance, often also denoted as Wasserstein distance.

Wasserstein Distance

Consider a fixed finite space \mathcal{X}=\left\{x_{1},\dots,x_{N}\right\} with a metric d:\mathcal{X}\times\mathcal{X}\rightarrow[0,\infty). Every probability measure on \mathcal{X} is given by a vector \bm{r} in

\mathcal{P}_{\mathcal{X}}=\left\{\bm{r}=(r_{x})_{x\in\mathcal{X}}\in\mathbb{R}% _{\geq 0}^{\mathcal{X}}:\sum_{x\in\mathcal{X}}r_{x}=1\right\},

via P_{\bm{r}}(\{x\})=r_{x}. We will not distinguish between the vector \bm{r} and the measure it defines. For p\geq 1, the p-th Wasserstein distance between two probability measures \bm{r},\bm{s}\in\mathcal{P}_{\mathcal{X}} is defined as

W_{p}(\bm{r},\bm{s})=\left(\min_{\bm{w}\in\Pi(\bm{r},\bm{s})}\sum_{x,x^{\prime% }\in\mathcal{X}}d^{p}\!(x,x^{\prime})w_{x,x^{\prime}}\right)^{1/p}, (1)

where \Pi(\bm{r},\bm{s}) is the set of all probability measures on \mathcal{X}\times\mathcal{X} with marginal distributions \bm{r} and \bm{s}, respectively. The minimization in (1) can be written as a linear program

\min\sum_{x,x^{\prime}\in\mathcal{X}}w_{x,x^{\prime}}d^{p}\!(x,x^{\prime})% \quad\textbf{s.t.}\quad\sum_{x^{\prime}\in\mathcal{X}}w_{x,x^{\prime}}=r_{x},% \quad\sum_{x\in\mathcal{X}}w_{x,x^{\prime}}=s_{x^{\prime}},\quad w_{x,x^{% \prime}}\geq 0, (2)

with N^{2} variables w_{x,x^{\prime}} and 2N constraints, where the weights d^{p}\!(x,x^{\prime}) are known and have been precalculated.

2.1 Approximating the Wasserstein Distance

The idea of the proposed algorithm is to replace a probability measure \bm{r}\in\mathcal{P}(\mathcal{X}) with an empirical measure \hat{\bm{r}}_{S} based on i.i.d. picks X_{1},\dots,X_{S}\sim\bm{r} for some integer S:

\hat{r}_{S,x}=\frac{1}{S}\#\left\{k:X_{k}=x\right\},\quad x\in\mathcal{X} (3)

Likewise, replace \bm{s} with \hat{\bm{s}}_{S}. Then, use W_{p}(\hat{\bm{r}}_{S},\hat{\bm{s}}_{S}) as a random approximation of W_{p}(\bm{r},\bm{s}).

Algorithm 1 Statistical approximation of W_{p}(\bm{r},\bm{s})
1:  Input: Probability measures \bm{r},\bm{s}\in\mathcal{P}_{\mathcal{X}}, sample size S and number of repetitions B
2:  for i=1\dots B do
3:     Sample i.i.d. X_{1},\dots,X_{S}\sim\bm{r} and independently Y_{1},\dots,Y_{S}\sim\bm{s}
4:     \hat{r}_{S,x}\leftarrow\#\left\{k:X_{k}=x\right\}/S for all x\in\mathcal{X}
5:     \hat{s}_{S,x}\leftarrow\#\left\{k:Y_{k}=x\right\}/S for all x\in\mathcal{X}
6:     Compute \hat{W}^{(i)}\leftarrow W_{p}(\hat{\bm{r}}_{S},\hat{\bm{s}}_{S})
7:  end for
8:  Return: \hat{W}^{(S)}_{p}(\bm{r},\bm{s})\leftarrow B^{-1}\sum_{i=1}^{B}\hat{W}^{(i)}

In each of the B iterations in Algorithm 1, the Wasserstein distance between two sets of S point masses has to be computed. For the exact Wasserstein distance, two measures on N points need to be compared. If we take for example the super-cubic runtime of the auction algorithm as a basis, Algorithm 1 has worst case runtimes

\mathcal{O}(BS^{3}\log S)

compared to \mathcal{O}(N^{3}\log N) for the exact distance. This means a dramatic reduction of computation time if S (and B) are small compared to N.

The application of Algorithm 1 to other optimal transport distances is straightforward. One can simply replace W_{p}(\hat{\bm{r}}_{S},\hat{\bm{s}}_{S}) with the desired distance, e.g. the Sinkhorn distance (Cuturi, 2013), see also our numerical experiments below.

3 Theoretical results

We give general non-asymptotic guarantees for the quality of the approximation \hat{W}^{(S)}_{p}(\bm{r},\bm{s}) in terms of the expected L_{1}-error. That is, we give bounds of the form

E\left[\left|\hat{W}^{(S)}_{p}(\bm{r},\bm{s})-W_{p}(\bm{r},\bm{s})\right|% \right]\leq g(S,\mathcal{X},p), (4)

for some function g. We are particularly interested in the dependence of the bound on the size N of the space \mathcal{X} and on the sample size S as this determines how the number of sampling points S (and hence the computational efford of Algorithm 1) must be increased for increasing problem size N in order to retain (on average) a certain approximation quality. In a second step, we obtain deviation inequalities for \hat{W}^{(S)}(\bm{r},\bm{s}) via concentration of measure techniques.

Related work

The question of the convergence of empirical measures to the true measure in expected Wasserstein distance has been considered in detail by Boissard and Gouic (2014) and Fournier and Guillin (2014). The case of the underlying measures being different (that is, the convergence of EW_{p}(\hat{\bm{r}}_{S},\hat{\bm{s}}_{S}) to W_{p}(\bm{r},\bm{s}) when \bm{r}\neq\bm{s}) has not been considered to the best of our knowledge. Theorem 1 is reminicent of the main result of Boissard and Gouic (2014). However, we give a result here, which is explicitly tailored to finite spaces and makes explicit the dependence of the constants on the size N of the underlying space \mathcal{X}. In fact, when we consider finite spaces \mathcal{X} which are subsets of \mathbb{R}^{D} later in Theorem 3, we will see that in contrast to the results of Boissard and Gouic (2014), the rate of convergence (in S) does not change when the dimension gets large, but rather the dependence of the constants on N changes. This is a valuable insight as our main concern here is how the subsample size S (driving the computational cost) must be chosen when N grows in order to retain a certain approximation quality.

3.1 Expected absolute error

Recall that, for \delta>0 the covering number \mathcal{N}(\mathcal{X},\delta) of \mathcal{X} is defined as the minimal number of closed balls with radius \delta and centers in \mathcal{X} that is needed to cover \mathcal{X}. Note that in contrast to continuous spaces, \mathcal{N}(\mathcal{X},\delta) is bounded by N for all \delta>0.

Theorem 1.

Let \hat{\bm{r}}_{S} be the empirical measure obtained from i.i.d. samples X_{1},\dots,X_{S}\sim\bm{r}, then

E\left[W_{p}^{p}(\hat{\bm{r}}_{S},\bm{r})\right]\leq\mathcal{E}_{q}/\sqrt{S}, (5)

where the constant \mathcal{E}_{q}:=\mathcal{E}_{q}(\mathcal{X},p) is given by

\begin{split}\displaystyle\mathcal{E}_{q}=2^{p-1}q^{2p}(\>\mathrm{diam}(% \mathcal{X}))^{p}\left(q^{-(l_{\max}+1)p}\sqrt{N}+\sum_{l=0}^{l_{\max}}q^{-lp}% \sqrt{\mathcal{N}(\mathcal{X},q^{-l}\>\mathrm{diam}(\mathcal{X}))}\right)\end{split} (6)

for any 2\leq q\in\mathbb{N} and l_{\max}\in\mathbb{N}.

Remark 1.

Since Theorem 1 holds for any integer q\geq 2 and l_{\max}\in\mathbb{N}, they can be chosen freely to minimize the constant \mathcal{E}_{q}. In the proof they appear as the branching number and depth of a spanning tree that is constructed on \mathcal{X} (see appendix).

Based on Theorem 1, we can formulate a bound for the mean approximation error of Algorithm 1.

Theorem 2.

Let \hat{W}^{(S)}_{p}(\bm{r},\bm{s}) be as in Algorithm 1 for any choice of B\in\mathbb{N}. Then for every integer q\geq 2

E\left[\left|\hat{W}^{(S)}_{p}(\bm{r},\bm{s})-W_{p}(\bm{r},\bm{s})\right|% \right]\leq 2\mathcal{E}_{q}^{1/p}S^{-1/(2p)}. (7)

Measures on Euclidean Space

While the constant \mathcal{E}_{q} in Theorem 1 may be difficult to compute or estimate in general, we give explicit bounds in the case when \mathcal{X} is a finite subset of a Euclidean space. They exhibit the dependence of the approximation error on the size of the space N. In particular, it comprises the case when the measures represent images (two- or more dimensional).

Theorem 3.

Let \mathcal{X} be a bounded subset of \mathbb{R}^{D} and let the metric d on \mathcal{X} be the usual Euclidean metric. Then,

\mathcal{E}_{q}\leq 2^{p+D}q^{2p+2}(\>\mathrm{diam}(\mathcal{X}))^{p}\cdot C_{% D,p}(N),


C_{D,p}(N)=\begin{cases}1&\text{if }D/2-p<0,\\ 1+\frac{1}{p}\log_{q}N&\text{if }D/2-p=0,\\ 1+N^{\frac{1}{2}(1-\frac{2p}{D})}&\text{if }D/2-p>0.\end{cases}

This result gives control over the error made by the approximation \hat{W}^{(S)}_{p}(\bm{r},\bm{s}) of W_{p}(\bm{r},\bm{s}). Of particular interest is the behavior of this error for high resolution images, that is N\rightarrow\infty. We distinguish three cases. In the low-dimensional case p^{\prime}=D/2-p<0, we have C_{D,p}(N)=\mathcal{O}(1). Hence, in this case, the approximation error is \mathcal{O}(S^{-\frac{1}{2p}}) independent of the size of the image. In the critical case p^{\prime}=0 the approximation error is no longer independent of N but is of order \mathcal{O}\left(\log(N)S^{-\frac{1}{2p}}\right). Finally, in the high-dimensional case the dependence on N becomes stronger with an approximation error of order

\mathcal{O}\left(\left(\frac{N^{(1-\frac{2p}{D})}}{S}\right)^{\frac{1}{2p}}% \right).

In all cases one can choose S=o(N) while still guaranteeing vanishing approximation error for N\rightarrow\infty. In practice, this means that for large images, S can typically be chosen (much) smaller than N to obtain a good approximation of the Wasserstein distance.

While the three cases in Theorem 3 resemble those given by Boissard and Gouic (2014), the rate of convergence in S as seen in Theorem 1 is \mathcal{O}(S^{-1/2}), regardless of the dimension of the underlying space \mathcal{X}.

Remark 2.

The results presented here extend to the case where \mathcal{X} is a bounded, countable subset of \mathbb{R}^{D}. However, \mathcal{E}_{q} can only be bounded in the low dimensional case (D/2-p<0) due to Theorem 3 and is infinite otherwise.

3.2 Concentration bounds

Based on the bounds for the expected approximation error we now give non-asymptotic guarantees for the approximation error in the form of deviation bounds using standard concentration of measure techniques.

Theorem 4.

If \hat{W}^{(S)}_{p}(\bm{r},\bm{s}) is obtained from Algorithm 1, then for every z\geq 0

P\left[|\hat{W}^{(S)}_{p}(\bm{r},\bm{s})-W_{p}(\bm{r},\bm{s})|\geq z+\frac{2% \mathcal{E}_{q}^{1/p}}{S^{\nicefrac{{1}}{{2p}}}}\right]\leq 2\exp\left(-\frac{% SBz^{2p}}{8\>\>\mathrm{diam}(\mathcal{X})^{2p}}\right). (8)

Note that while the mean approximation quality 2\mathcal{E}_{q}^{1/p}/S^{1/(2p)} only depends on the subsample size S, the stochastic variability (see the right hand side term in (8)) depends on the product SB. This means that the repetition number B cannot decrease the expected error but it decreases the magnitude of fluctuation around it.

4 Simulations

This section covers the numerical findings of the simulations. Runtimes and returned values of Algorithm 1 for each back-end solver are reported in relation to the results of that solver on the original problem. Four different solvers are tested.

4.1 Simulation Setup

The setup of our simulations is identical to (Schrieber et al., 2017). One single core of a Linux server (AMD Opteron Processor 6140 from 2011 with 2.6 GHz) was used. The original and subsampled instances were run under the same conditions.

Three of the four methods featured in this simulation are exact linear programming solvers. The transportation simplex is a modified version of the network simplex solver tailored towards optimal transport problems. Details can be found for example in (Luenberger and Ye, 2008). The shortlist method (Gottschlich and Schuhmacher, 2014) is a modification of the transportation simplex, that performs an additional greedy step to quickly find a good initial solution. The parameters were chosen as the default parameters described in that paper. The third method is the network simplex solver of CPLEX (www.ibm.com/software/commerce/optimization/cplex-optimizer/). For the transportation simplex and the shortlist method the implementations provided in the R package transport (Schuhmacher et al., 2014) were used. The models for the CPLEX solver were created and solved via the R package Rcplex (Bravo and Theussl, 2016).

Additionally, the Sinkhorn scaling algorithm (Cuturi, 2013) was tested in our simulation. This method computes an entropy regularized optimal transport distance. The regularization parameter was chosen according to the heuristic in Cuturi (2013). Note that the Sinkhorn distance is not covered by the theoretical results from Section 3. The errors reported for the Sinkhorn scaling are relative to the values returned by the algorithm on the full problems, which themselves differ from the actual Wasserstein distances.

The instances of optimal transport considered here are discrete instances of two differend types: regular grids in two dimensions, that means images in various resolutions, as well as point clouds in \left[0,1\right]^{D} with dimensions D=2, 3 and 4. For the image case, three instances were chosen from the DOTmark: two images of each of the classes White Noise, Cauchy Density, and Classic Images, which are then treated in the three resolutions 32\times 32, 64\times 64 and 128\times 128. In the White Noise class the grayscale values of the pixels are independent of each other, the Cauchy Density images show bivariate Cauchy densities with random centers and varying scale ellipses, while Classic Images contains grayscale test images used in imaging. See (Schrieber et al., 2017) for further details on the different image classes and example images. The instances were chosen to cover different types of images, while still allowing for the simulation of a large variety of parameters for subsampling.

The point cloud type instances were created as follows: The support points of the measures are independently, uniformly distributed on \left[0,1\right]^{D}. The number of points N was chosen 32^{2}, 64^{2} and 128^{2} in order to match the size of the grid based instances. For each choice of D and N, three instances were generated with regards to the three images types used in the grid based case. Two measures on the points are drawn from the Dirichlet distribution with all parameters equal to one. That means, the masses on different points are independent of each other, similar to the white noise images. To create point cloud versions of the Cauchy Density and Classic Images classes the grayscale values of the same images were used to get the mass values for the support points. In three and four dimensions, the product measure of the images with their sum of columns and with themselves, respectively, was used.

All original instances were solved by each back-end solver in each resolution for the values p=1, p=2, and p=3 in order to be compared to the approximative results for the subsamples in terms of runtime and accuracy, with the exception of CPLEX, where the 128\times 128 instances could not be solved due to memory limitations. Algorithm 1 was applied to each of these instances with parameters S\in\{100,500,1000,2000,4000\} and B\in\{1,2,5\}. For every combination of instance and parameters, the subsampling algorithm was run 5 times in order to mitigate the randomness of the results.

Since the linear programming solvers had a very similar performance on the grid based instances (see below), only one of them - the transportation simplex - was tested on the point cloud instances.

4.2 Computational Results

Figure 2: Relative errors \lvert\hat{W}^{(S)}_{p}(\bm{r},\bm{s})-W_{p}(\bm{r},\bm{s})\rvert/W_{p}(\bm{r}% ,\bm{s}) vs. relative runtimes \lvert\hat{t}-t\rvert/t for different parameters S and B and different problem sizes for images. \hat{t} is the runtime of Algorithm 1 and t is the runtime of the respective back-end solver without subsampling.
Figure 3: Relative errors vs. relative runtimes for different parameters S and B and different problem sizes for point clouds. The number of support points matches the number of pixels in the images.

As mentioned before, all results of Algorithm 1 are relative to the results of the methods applied to the original problems. We are mainly interested in the reduction in runtime and accuracy of the returned values. Many important results can be observed in Figure 2 and 3. The points in the diagram represent averages over the different methods, instances, and multiple tries, but are separated in resolution and choices of the parameters S and B in Algorithm 1.

For images we observe a decrease in relative runtimes with higher resolution, while the average relative error is independent of the image resolution. In the point cloud case, however, the relative error increases slightly with the instance size. The number S of sampled points seems to considerably affect the relative error. An increase of the number of points results in more accurate values, with average relative errors as low as about 3\% for S=4000, while still maintaining a speedup of two orders of magnitude on 128\times 128 images. Lower sample sizes yield higher average errors, but also lower runtimes. With S=500 the runtime is reduced by over four orders of magnitude with an average relative error of less than 10\%. As to be expected, runtime increases linearly with the number of repetitions B. However, the impact on the relative errors is rather inconsistent. This is due to the fact, that the costs returned by the subsampling algorithm are often overestimated, therefore averaging over multiple tries does not yield improvements (see Figure 4). That means in order to increase the accuracy of the algorithm it is advisable to keep B=1 and instead increse the sample size S. However, increasing B can be useful to lower the variability of the results.

Figure 4: The signed relative approximation error \left(\hat{W}^{(S)}_{p}(\bm{r},\bm{s})-W_{p}(\bm{r},\bm{s})\right)/W_{p}(\bm{r% },\bm{s}) showing that the approximation overestimates the exact distance for small S but the bias vanishes for larger S.

On the contrary, there is a big difference in accuracy between the image classes. While Algorithm 1 has consistently low relative errors on the Cauchy Density images, the exact optimal costs for White Noise images cannot be approximated as reliably. The relative errors fluctuate more and are generally much higher, as one can see from Figure 5 (left). In images with smooth structures and regular features the subsamples are able to capture that structure and therefore deliver a more precise representation of the images and a more precise value. This is not possible in images that are very irregular or noisy, such as the White Noise images, which have no structure to begin with. The Classic Images contain both regular structures and more irregular regions, therefore their relative errors are slightly higher than in the Cauchy Density cases. The algorithm has a similar performance on the point cloud instances, that are modelled after the Cauchy Density and Classic Images classes, while the Dirichlet instances have a more desirable accuracy compared to the White Noise images, as seen in Figure 5 (right).

Figure 5: A comparison of the relative errors for different image classes (left) and and point cloud instance classes (right).
Figure 6: A comparison between the approximations of the Wasserstein and Sinkhorn distances.

There are no significant differences in performance between the different back-end solvers for the Wasserstein distance. As Figure 6 shows, accuracy seems to be better for the Sinkhorn distance compared to the other three solvers which report the exact Wasserstein distance.

Figure 7: A comparison of the mean relative errors in the point cloud instances with sample size S=4000 for different values of p^{\prime}=\nicefrac{{D}}{{2}}-p.

In the results of the point cloud instances we can observe the influence of the value p^{\prime}=\nicefrac{{D}}{{2}}-p on the scaling of the relative error with the instance size N for constant sample size (S=4000). This is shown in Figure 7. We observe an increase of the relative error with p^{\prime}, as expected from the theory. However, we are not able to clearly distinguish between the three cases p^{\prime}<0, p^{\prime}=0 and p^{\prime}>0. This might be due to the relatively small instance sizes N in the experiments. While we see that the relative errors are independent of N in the image case (compare Figure 2), for the point clouds N has an influence on the accuracy that depends on p^{\prime}.

5 Discussion

As our simulations demonstrate, subsampling is a simple, yet powerful tool to obtain good approximations to Wasserstein distances with only a small fraction of required runtime and memory. It is especially remarkable that in the case of two dimensional images for a fixed amount of subsampled points, and therefore a fixed amount of time and memory, the relative error is independent of the resolution/size of the images. Based on these results, we expect the subsampling algorithm to return similarly precise results with even higher resolutions of the images it is applied to, while the effort to obtain them stays the same. Even in point cloud instances the relative error only scales mildly with the original input size N and is dependent on the value p^{\prime}.

The numerical results (Figure 2) show an inverse polynomial decrease of the approximation error with S, in accordance with the theoretical results. As we see little dependence on the cost exponent p we speculate that the rate O(S^{-1/2p}) might be improved upon. In fact, recent results on the asymptotic distribution of the empirical Wasserstein distance would suggest an \mathcal{O}(S^{-1/2}) rate (Sommerfeld and Munk, 2016).

When applying the algorithm, it is important to note that the quality of the returned values depend on the structure of the data. In very irregular instances it is necessary to increase the sample size in order to obtain similarly precise results, while in regular structures a small sample size suffices.

Our scheme allows the parameters to be easily tuned towards faster runtimes or more precise results, as desired. Increases and decreases of the sample size S are recommended to influence the performance in either direction, while the parameter B should only be increased, if a particularly low variability of the estimate is required or if the repetitions can be computed in parallel. Otherwise, the higher runtime should be spent with a higher sample size (compare Figure 2).

The scheme presented here can readily be applied to other optimal transport distances, as long as a solver is available, as we demonstrated with the Sinkhorn distance (Cuturi, 2013). Empirically, we can report good performance in this case, suggesting that entropically regularized distances might be even more amenable to subsampling approximation than the Wasserstein distance itself. Extending the theoretical results to this case would require an analysis of the mean speed of convergence of empirical Sinkhorn distances, which is an interesting task for future research.

All in all, subsampling proves to be a general, powerful and versatile tool that can be used with virtually any optimal transport solver as back-end and has both theoretical approximation error guarantees, and a convincing performance in practice. It is a challenge to extend this method in a way which is specifically tailored to the geometry of the underlying space \mathcal{X}, which may result in further improvements.


Proof of Theorem 1

Proof strategy

The method used in this proof has been employed before to bound the mean rate of convergence of the empirical Wasserstein distance on a general metric space (\mathcal{X},d) (Boissard and Gouic, 2014; Fournier and Guillin, 2014). In essence, it constructs a tree on the space \mathcal{X} and bounds the Wasserstein distance with some transport metric in the tree, which can either be computed explicitly or bounded easily.

More precisely, in our case of finite spaces, let \mathcal{T} be a spanning tree on \mathcal{X} (that is, a tree with vertex set \mathcal{X} and edge lengths given by the metric d on \mathcal{X}) and d_{\mathcal{T}} the metric on \mathcal{X} defined by the path lengths in the tree. Clearly, the tree metric d_{\mathcal{T}} dominates the original metric d on \mathcal{X} and hence W_{p}(\bm{r},\bm{s})\leq W_{p}^{\mathcal{T}}(\bm{r},\bm{s}) for all \bm{r},\bm{s}\in\mathcal{P}(\mathcal{X}), where W_{p}^{\mathcal{T}} denotes the Wasserstein distance evaluated with respect to the tree metric. The goal is now to bound E\left[(W_{p}^{\mathcal{T}}(\hat{\bm{r}}_{S},\bm{r}))^{p}\right].

Assume \mathcal{T} is rooted at \mathrm{root}(\mathcal{T})\in\mathcal{X}. Then, for x\in\mathcal{X} and x\neq\mathrm{root}(\mathcal{T}) we may define \mathrm{par}(x)\in\mathcal{X} as the immediate neighbor of x in the unique path connecting x and \mathrm{root}(\mathcal{T}). We set \mathrm{par}(\mathrm{root}(\mathcal{T}))=\mathrm{root}(\mathcal{T}). We also define \mathrm{children}(x) as the set of vertices x^{\prime}\in\mathcal{X} such that there exists a sequence x^{\prime}=x_{1},\dots,x_{l}=x\in\mathcal{X} with \mathrm{par}(x_{j})=x_{j+1} for j=1,\dots,l-1. Note that with this definition x\in\mathrm{children}(x). Additionally, define the linear operator S_{\mathcal{T}}\colon\mathbb{R}^{\mathcal{X}}\rightarrow\mathbb{R}^{\mathcal{X}}

(S_{\mathcal{T}}{\bm{u}})_{x}=\sum_{x^{\prime}\in\mathrm{children}(x)}u_{x^{% \prime}}. (9)

Building the tree

We build a q-ary tree on \mathcal{X}. For l\in\{0,\dots,\l_{\max}\} we let Q_{l}\subset\mathcal{X} be the center points of a q^{-l}\>\mathrm{diam}(\mathcal{X}) covering of \mathcal{X}, that is

\bigcup_{x\in Q_{l}}B(x,q^{-l}\>\mathrm{diam}(\mathcal{X}))=\mathcal{X},\text{% and }|Q_{l}|=\mathcal{N}(\mathcal{X},q^{-l}\>\mathrm{diam}(\mathcal{X})),

where B(x,\epsilon)=\{x^{\prime}\in\mathcal{X}:d(x,x^{\prime})\leq\epsilon\}. Additionally set Q_{l_{\max}+1}=\mathcal{X}. Now define \tilde{Q}_{l}=Q_{l}\times\{l\} and we will build a tree structure on \cup_{l=0}^{\l_{\max}+1}\tilde{Q}_{l}.

Since we must have |\tilde{Q}_{0}|=1 we can take this element as the root. Assume now that the tree already contains all elements of \cup_{j=0}^{l}\tilde{Q}_{j} Then, we add to the tree all elements of \tilde{Q}_{l+1} by choosing for (x,l+1)\in\tilde{Q}_{l+1} (exactly one) parent element (x^{\prime},l)\in\tilde{Q}_{l} such that d(x,x^{\prime})\leq q^{-l}\>\mathrm{diam}(\mathcal{X}). This is possible, since Q_{l} is a q^{-l}\>\mathrm{diam}(\mathcal{X}) covering of \mathcal{X}. We set the length of the connecting edge to q^{-l}\>\mathrm{diam}(\mathcal{X}).

In this fashion we obtain a spanning tree \mathcal{T} of \cup_{l=0}^{l_{\max}+1}\tilde{Q}_{l} and a partition \{\tilde{Q}_{l}\}_{l=0,\dots,l_{\max}+1}. About this tree we know that

  • it is in fact a tree. First, it is connected, because the construction starts with one connected component and in every subsequent step all additional vertices are connected to it. Second, it contains no cycles. To see this let ((x_{1},l_{1}),\dots,(x_{K},l_{K})) a cycle in \mathcal{T}. Without loss of generality we may assume l_{1}=\min\{l_{1},\dots,l_{K}\}. Then, (x_{1},l_{1}) must have at least two edges connecting it to vertices in a \tilde{Q}_{l} with l\geq l_{1} which is impossible by construction.

  • |\tilde{Q}_{l}|=\mathcal{N}(\mathcal{X},q^{-l}\>\mathrm{diam}(\mathcal{X})) for 0\leq l\leq l_{\max}.

  • d(x,\mathrm{par}(x))=q^{-l+1}\>\mathrm{diam}(\mathcal{X}) whenever x\in\tilde{Q}_{l}.

  • d(x,x^{\prime})\leq d_{\mathcal{T}}\left((x,l_{\max}+1),(x^{\prime},l_{\max}+1% )\right).

Since the leafs of \mathcal{T} can be identified with \mathcal{X} a measure \bm{r}\in\mathcal{P}(\mathcal{X}) canonically defines a probability measure \bm{r}^{\mathcal{T}}\in\mathcal{P}(\mathcal{T}) for which r^{\mathcal{T}}_{(x,\l_{\max}+1)}=r_{x} and r^{\mathcal{T}}_{(x,l)}=0 for l\leq l_{\max}. In slight abuse of notation we will denote the measure \bm{r}^{\mathcal{T}} simply by \bm{r}. With this notation, we have W_{p}(\bm{r},\bm{s})\leq W_{p}^{\mathcal{T}}(\bm{r},\bm{s}) for all \bm{r},\bm{s}\in\mathcal{P}(\mathcal{X}).

Wasserstein distance on trees

Note also that \mathcal{T} is ultra-metric that is, all its leaves are at the same distance from the root. For trees of this type, we can define a height function h:\mathcal{X}\rightarrow[0,\infty) such that h(x)=0 if x\in\mathcal{X} is a leaf and h(\mathrm{par}(x))-h(x)=d_{\mathcal{T}}(x,\mathrm{par}(x)) for all x\in\mathcal{X}\setminus\mathrm{root}(\mathcal{X}). There is an explicit formula for the Wasserstein distance on ultra-metric trees (Kloeckner, 2013). Indeed, if \bm{r},\bm{s}\in\mathcal{P}(\mathcal{X}) then

(W_{p}^{\mathcal{T}}(\bm{r},\bm{s}))^{p}=2^{p-1}\sum_{x\in\mathcal{X}}\left(h(% \mathrm{par}(x))^{p}-h(x)^{p}\right)\left|(S_{\mathcal{T}}\bm{r})_{x}-(S_{% \mathcal{T}}\bm{s})_{x}\right|. (10)

with the operator S_{\mathcal{T}} as defined in (9). For the tree \mathcal{T} constructed above and x\in\tilde{Q}_{l} with l=0,\dots,l_{\max} we have


and therefore \>\mathrm{diam}(\mathcal{X})q^{-l}\leq h(x)\leq 2\>\mathrm{diam}(\mathcal{X})q% ^{-l}. This yields

(h(\mathrm{par}(x))^{p}-(h(x))^{p})\leq(\>\mathrm{diam}(\mathcal{X}))^{p}q^{-(% l-2)p}.

Then (10) yields

\displaystyle E\left[W_{p}^{p}(\hat{\bm{r}}_{S},\bm{r})\right]\leq 2^{p-1}q^{2% p}(\>\mathrm{diam}(\mathcal{X}))^{p}\sum_{l=0}^{l_{\max}+1}q^{-lp}\sum_{x\in% \tilde{Q}_{l}}E|(S_{\mathcal{T}}\hat{\bm{r}}_{S})_{x}-(S_{\mathcal{T}}\bm{r})_% {x}|.

Since (S_{\mathcal{T}}\hat{\bm{r}}_{S})_{x} is the mean of S i.i.d. Bernoulli variables with expectation (S_{\mathcal{T}}\bm{r})_{x} we have

\displaystyle\sum_{x\in\tilde{Q}_{l}}E|(S_{\mathcal{T}}\hat{\bm{r}}_{S})_{x}-(% S_{\mathcal{T}}\bm{r})_{x}|\leq\sum_{x\in\tilde{Q}_{l}}\sqrt{\frac{(S_{% \mathcal{T}}\bm{r})_{x}(1-(S_{\mathcal{T}}\bm{r})_{x})}{S}}\\ \displaystyle\leq\frac{1}{\sqrt{S}}\left(\sum_{x\in\tilde{Q}_{l}}(S_{\mathcal{% T}}\bm{r})_{x}\right)^{1/2}\left(\sum_{x\in\tilde{Q}_{l}}(1-(S_{\mathcal{T}}% \bm{r})_{x})\right)^{1/2}\leq\sqrt{|\tilde{Q}_{l}|/S},

using Hölder’s inequality and the fact that \sum_{x\in\tilde{Q}_{l}}(S_{\mathcal{T}}\bm{r})_{x}=1 for all l=0,\dots,\l_{\max}+1. This finally yields

\displaystyle E\left[W_{p}^{p}(\hat{\bm{r}}_{S},\bm{r})\right]\leq 2^{p-1}q^{2% p}(\>\mathrm{diam}(\mathcal{X}))^{p}\left(q^{-(l_{\max}+1)p}\sqrt{N}+\sum_{l=0% }^{l_{\max}}q^{-lp}\sqrt{\mathcal{N}(\mathcal{X},q^{-l}\>\mathrm{diam}(% \mathcal{X}))}\right)/\sqrt{S}\\ \displaystyle\leq\mathcal{E}_{q}(\mathcal{X},p)/\sqrt{S}.

Proof of Theorem 2

The statement of the theorem is an immediate consequence of the reverse triangle inequality for the Wasserstein distance, Jensen’s inequality and Theorem 1,

\displaystyle E\left[\left|\hat{W}^{(S)}_{p}(\bm{r},\bm{s})-W_{p}(\bm{r},\bm{s% })\right|\right]\leq E\left[W_{p}(\hat{\bm{r}}_{S},\bm{r})+W_{p}(\hat{\bm{s}}_% {S},\bm{s})\right]\\ \displaystyle\leq E\left[W_{p}^{p}(\hat{\bm{r}}_{S},\bm{r})\right]^{1/p}+E% \left[W_{p}^{p}(\hat{\bm{s}}_{S},\bm{s})\right]^{1/p}\leq 2\mathcal{E}_{q}^{1/% p}/S^{1/(2p)}.

Proof of Theorem 3

We assume without loss of generality that \mathcal{X}\subseteq[0,\>\mathrm{diam}(\mathcal{X})]^{D}. First, note that \mathcal{N}([0,\>\mathrm{diam}(\mathcal{X})]^{D},\epsilon)\leq 2^{D}\>\mathrm{% diam}(\mathcal{X})^{D}\epsilon^{-D}, as shown for example in (Shalev-Shwartz and Ben-David, 2014, Example 27.1). Therefore,

\mathcal{N}(\mathcal{X},q^{-l}\>\mathrm{diam}(\mathcal{X}))\leq\mathcal{N}([0,% \>\mathrm{diam}(\mathcal{X})]^{D},q^{-l}\>\mathrm{diam}(\mathcal{X})/2)\leq 2^% {2D}q^{lD}.

This yields

\sum_{l=0}^{l_{\max}}q^{-lp}\sqrt{\mathcal{N}(\mathcal{X},q^{-l}\>\mathrm{diam% }(\mathcal{X}))}\leq 2^{D}\sum_{l=0}^{l_{\max}}q^{l(D/2-p)}\\ \leq 2^{D}q\cdot\begin{cases}(1+q^{l_{\max}(D/2-p)})&\text{if }D/2-p\neq 0,\\ (l_{\max})&\text{if }D/2-p=0.\end{cases}

Setting l_{\max}=\lceil\beta\log_{q}N\rceil for some \beta>0 (to be specified later) and using (6) yields

\mathcal{E}_{q}\leq 2^{D+p-1}q^{2p+2}(\>\mathrm{diam}(\mathcal{X}))^{p}\cdot% \begin{cases}(1+N^{-\beta p+1/2}+N^{\beta(D/2-p)})&\text{if }D/2-p\neq 0\\ (1+N^{-\beta p+1/2}+\beta\log_{q}N)&\text{if }D/2-p=0.\end{cases}

If D/2-p<0 we can choose \beta large enough such that 1+N^{-\beta p+1/2}+N^{\beta(D/2-p)}\leq 2. If D/2-p>0 we choose \beta=1/D such that 1+N^{-\beta p+1/2}+N^{\beta(D/2-p)}\leq 1+N^{\frac{1}{2}(1-2p/D)}. Finally, for D/2-p=0 we set \beta=1/(2p) such that 1+N^{-\beta p+1/2}+\beta\log_{q}N\leq 2+\frac{1}{p}\log_{q}N. This concludes the proof.

Proof of Theorem 4

We introduce some additional notation. For (x,y),(x^{\prime},y^{\prime})\in\mathcal{X}^{2} we set

d_{\mathcal{X}^{2}}((x,y),(x^{\prime},y^{\prime}))=\left\{d^{p}(x,x^{\prime})+% d^{p}(y,y^{\prime})\right\}^{1/p}

We further define the function Z:(\mathcal{X}^{2})^{SB}\rightarrow\mathbb{R} via

\begin{split}\displaystyle\left((x_{11},y_{11}),\dots,(x_{SB},y_{SB})\right)% \mapsto\frac{1}{B}\sum_{i=1}^{B}\left[W_{p}\left(\frac{1}{S}\sum_{j=1}^{S}% \delta_{x_{ji}},\frac{1}{S}\sum_{j=1}^{S}\delta_{y_{ji}}\right)-W_{p}(\bm{r},% \bm{s})\right].\end{split}

Since W_{p}^{p}(\cdot,\cdot) is jointly convex (Villani, 2008, Thm.4.8), we have

W_{p}\left(\frac{1}{S}\sum_{j=1}^{S}\delta_{x_{j}},\frac{1}{S}\sum_{j=1}^{S}% \delta_{y_{j}}\right)\leq\left\{\frac{1}{S}\sum_{j=1}^{S}W_{p}^{p}(\delta_{x_{% j}},\delta_{y_{j}})\right\}^{1/p}=S^{-1/p}\left\{\sum_{j=1}^{S}d^{p}(x_{j},y_{% j})\right\}^{1/p}.

Our first goal is to show that Z is Lipschitz continuous. To this end, let ((x_{11},y_{11}),\dots,(x_{SB},y_{SB})) and ((x^{\prime}_{11},y^{\prime}_{11}),\dots,(x^{\prime}_{SB},y^{\prime}_{SB})) arbitrary elements of (\mathcal{X}^{2})^{SB}. Then, using the reverse triangle inequality and the relations above

\displaystyle|Z((x_{11},y_{11}),\dots,(x_{SB},y_{SB}))-Z((x^{\prime}_{11},y^{% \prime}_{11}),\dots,(x^{\prime}_{SB},y^{\prime}_{SB}))|
\displaystyle\leq\frac{1}{B}\sum_{i=1}^{B}\bigg{|}W_{p}\left(\frac{1}{S}\sum_{% j=1}^{S}\delta_{x_{ji}},\frac{1}{S}\sum_{j=1}^{S}\delta_{y_{ji}}\right)-W_{p}% \left(\frac{1}{S}\sum_{j=1}^{S}\delta_{x^{\prime}_{ji}},\frac{1}{S}\sum_{j=1}^% {S}\delta_{y^{\prime}_{ji}}\right)\bigg{|}
\displaystyle\leq\frac{1}{B}\sum_{i=1}^{B}\bigg{[}W_{p}\left(\frac{1}{S}\sum_{% j=1}^{S}\delta_{x_{ji}},\frac{1}{S}\sum_{j=1}^{S}\delta_{x^{\prime}_{ji}}% \right)+W_{p}\left(\frac{1}{S}\sum_{j=1}^{S}\delta_{y_{ji}},\frac{1}{S}\sum_{j% =1}^{S}\delta_{y^{\prime}_{ji}}\right)\bigg{]}
\displaystyle\leq\frac{S^{-1/p}}{B}\sum_{i=1}^{B}\bigg{[}\left\{\sum_{j=1}^{S}% d^{p}(x_{ji},x^{\prime}_{ji})\right\}^{1/p}+\left\{\sum_{j=1}^{S}d^{p}(y_{ji},% y^{\prime}_{ji})\right\}^{1/p}\bigg{]}
\displaystyle\leq\frac{S^{-1/p}}{B}\left(2B\right)^{\frac{p-1}{p}}\left\{\sum_% {i,j}d^{p}_{\mathcal{X}^{2}}((x_{ji},y_{ji}),(x^{\prime}_{ji},y^{\prime}_{ji})% )\right\}^{1/p}

Hence, Z/2 is Lipschitz continuous with constant (SB)^{-1/p} relative to the p-metric generated by d_{\mathcal{X}^{2}} on (\mathcal{X}^{2})^{SB}.

For \tilde{\bm{r}}\in\mathcal{P}({\mathcal{X}^{2}}) let H(\cdot\>|\>\tilde{\bm{r}}) denote the relative entropy with respect to \tilde{\bm{r}}. Since \mathcal{X}^{2} has d_{\mathcal{X}^{2}}-diameter 2^{1/p}\>\mathrm{diam}(\mathcal{X}), we have by (Bolley and Villani, 2005, Particular case 2.5, page 337) that for every \tilde{\bm{s}}

W_{p}(\tilde{\bm{r}},\tilde{\bm{s}})\leq\left(8\>\mathrm{diam}(\mathcal{X})^{2% p}H(\tilde{\bm{r}}\>|\>\tilde{\bm{s}})\right)^{1/2p}. (11)

If X_{11},\dots,X_{SB}\sim\bm{r} and Y_{11},\dots,Y_{SB}\sim\bm{s} are all independent, we have

Z((X_{11},Y_{11}),\dots,(X_{SB},Y_{SB}))\sim\hat{W}^{(S)}_{p}(\bm{r},\bm{s})-W% _{p}(\bm{r},\bm{s}).

The Lipschitz continuity of Z and the transportation inequality (11) yields a concentration result for this random variable. In fact, by (Gozlan and Léonard, 2007, Lemma 6) we have

P\bigg{[}\hat{W}^{(S)}_{p}(\bm{r},\bm{s})-W_{p}(\bm{r},\bm{s})\geq E\left[\hat% {W}^{(S)}_{p}(\bm{r},\bm{s})-W_{p}(\bm{r},\bm{s})\right]+z\bigg{]}\leq\exp% \left(\frac{-SBz^{2p}}{8\>\>\mathrm{diam}(\mathcal{X})^{2p}}\right).

for all z\geq 0. Note that -Z is Lipschitz continuous as well and hence, by the union bound,

P\bigg{[}\left|\hat{W}^{(S)}_{p}(\bm{r},\bm{s})-W_{p}(\bm{r},\bm{s})\right|% \geq E\left[\left|\hat{W}^{(S)}_{p}(\bm{r},\bm{s})-W_{p}(\bm{r},\bm{s})\right|% \bigg{]}+z\right]\leq 2\exp\left(\frac{-SBz^{2p}}{8\>\>\mathrm{diam}(\mathcal{% X})^{2p}}\right).

Now, with the reverse triangle inequality, Jensen’s inequality and Theorem 1,

\displaystyle E\left[\left|\hat{W}^{(S)}_{p}(\bm{r},\bm{s})-W_{p}(\bm{r},\bm{s% })\right|\right]\leq E\left[W_{p}(\hat{\bm{r}}_{S},\bm{r})+W_{p}(\hat{\bm{s}}_% {S},\bm{s})\right]\\ \displaystyle\leq E\left[W_{p}^{p}(\hat{\bm{r}}_{S},\bm{r})\right]^{1/p}+\left% [W_{p}^{p}(\hat{\bm{s}}_{S},\bm{s})\right]^{1/p}\leq 2\mathcal{E}_{q}^{1/p}/S^% {1/(2p)}.

Together with the last concentration inequality above, this concludes the proof of Theorem 4.


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