Two days ago I wrote about random points in a square. At the last minute I added the paragraph asking about the generalization to random points in a cube. I have to admit that I didn't check the Web to see what was known about the question.

Shortly after my post appeared, Pieterjan Robbe of KU Leuven in Belgium submitted a comment pointing out that the problem has been studied extensively. He provided several pointers. The solution even has a name; it's called Robbins Constant. I have listed a few of the references below.

Contents

Robbins Constant

Robbins Constant is the value of this six-fold integral.

$$\delta = \int_0^1 \int_0^1 \int_0^1 \int_0^1 \int_0^1 \int_0^1 \! \sqrt{(x_1-y_1)^2 + (x_2-y_2)^2 + (x_3-y_3)^2} \ \mathrm{d}x_1 \mathrm{d}y_1 \mathrm{d}x_2 \mathrm{d}y_2 \mathrm{d}x_3 \mathrm{d}y_3 $$

A change of variables makes it a triple integral.

$$\delta = 8 \int_0^1\int_0^1 \int_0^1 \! \sqrt{x^2 + y^2 + z^2} \ (1-x)(1-y)(1-z) \ \mathrm{d}x \mathrm{d}y \mathrm{d}z$$

Numerical Integration

Numerical integration with the default tolerances gets about nine decimal places.

   format long
   F = @(x,y,z) sqrt(x.^2+y.^2+z.^2).*(1-x).*(1-y).*(1-z)
   delta = 8*integral3(F,0,1,0,1,0,1)

F =
  function_handle with value:
    @(x,y,z)sqrt(x.^2+y.^2+z.^2).*(1-x).*(1-y).*(1-z)
delta =
   0.661707182095901

Analytic Solution

The exact analytic solution is impressive. I can't explain where it comes from.

$$\delta = \frac{1}{105}(4 + 17\sqrt{2} - 6\sqrt{3} + 21\log{(1+\sqrt{2})} + 84\log{(1+\sqrt{3})} - 42\log{(2)} - 7\pi)$$

Here is the numeric value.

   delta = (1/105)*(4 + 17*sqrt(2) - 6*sqrt(3) + 21*log(1+sqrt(2)) + ...
            84*log(1+sqrt(3)) - 42*log(2) - 7*pi)

delta =
   0.661707182267176

Hypercubes and Vectorize

I also received email from MathWorks colleague Matt Tearle informing me about vecnorm, a new function that is in MATLAB Release R2017b. Unlike the norm function that computes matrix norms, vecnorm treats the elements along a specified dimension as vectors and calculates the norm of each vector. Here is the documentation page.

I don't yet have R2017b on this computer, but Matt's email prompted me to think about vectorizing the simulation, even without vecnorm. While we're at it, let's generalize to d dimensional hypercubes. And, I'll make it a one-liner.

   n = 10000000;  % n samples.
   for d = 1:10   % d dimensions.
      delta = mean(sqrt(sum((rand(n,d)-rand(n,d)).^2,2)));
      fprintf('%6d %7.4f\n',d,delta)
   end

     1  0.3335
     2  0.5215
     3  0.6617
     4  0.7778
     5  0.8786
     6  0.9692
     7  1.0517
     8  1.1282
     9  1.1997
    10  1.2675

Thanks

Thanks to Pieterjan Robbe and Matt Tearle.

References

D. P. Robbins, Problem 2629, Average distance between two points in a box, Amer. Mathematical Monthly, 85.4, 1978, p. 278.

D. H. Bailey, J. M. Borwein and R. E. Crandall, Advances in the Theory of Box Integrals, <http://crd-legacy.lbl.gov/~dhbailey/dhbpapers/BoxII.pdf>.

Johan Philip, The Probability Distribution of the Distance Between Two Random Points in a Box, https://people.kth.se/~johanph/habc.pdf.

Eric W. Weisstein, Hypercube Line Picking. MathWorld, <http://mathworld.wolfram.com/HypercubeLinePicking.html>.

N. J. A. Sloane, editor, The On-Line Encyclopedia of Integer Sequences, Decimal Expansion of Robbins Constant, <http://oeis.org/A073012>.

Get the MATLAB code

Published with MATLAB® R2017a