Random Vectors with Fixed Sum

Randomly and uniformly generates vectors with a specified sum and values in a specified interval.

Description

This generates m random n-element column vectors of values, [x1;x2;...;xn], each with a fixed sum, s, and subject to a restriction a<=xi<=b. The vectors are randomly and uniformly distributed in the n-1 dimensional space of solutions. This is accomplished by decomposing that space into a number of different types of simplexes (the many-dimensional generalizations of line segments, triangles, and tetrahedra.) The 'rand' function is used to distribute vectors within each simplex uniformly, and further calls on 'rand' serve to select different types of simplexes with probabilities proportional to their respective n-1 dimensional volumes. This algorithm does not perform any rejection of solutions - all are generated so as to already fit within the prescribed hypercube.

• MATLAB release: MATLAB 5.2 (R10)

[John D'Errico]

This took a bit of work to verify uniformity in a slice of an n-dimensional hypercube. I'm now confident that Roger has done what he claimed, having checked samplings in several different dimensions, as well as having thought through the process he used to generate the sampling.

[Mike Edwards]

very useful! beautiful code!

[Per-Anders Ekström]

Excellent!

[Florian]

Exactly what I was looking for!!! Many thanks for the great work!!!

[Vassilios Vassiliadis]

Respect....

[Mate 2u]

Hi when i try to use the function on a very large a array it gives me the following error...

??? Maximum variable size allowed by the program is exceeded.

Error in ==> randfixedsum at 58
w = zeros(n,n+1); w(1,2) = realmax; % Scale for full 'double' range

[Bruno Luong]

 

[Christophe Lauwerys]

Nice. I'm trying to generate random data within a simplex defined by linear inequality constraints.

Lets say I already have the N vertices of the simplex defined by the inequalities. Is it then correct to first generate a random sample in the interval [0,1] with a sum equal to 1, and then take the inner product of this sample with the vector of vertices?

Something along the lines of:

X = rand(6,2);
k = convhull(X);
plot(X(k,1),X(k,2),'b'), hold on
nv = numel(k)-1; % Nmuber of vertices
X = X(k(1:end-1),:); % Remove repeated first vertex
L = randfixedsum(size(X,1),1000,1,0,1);
Y = L'*X;
plot(Y(:,1),Y(:,2),'r.'), hold off

Maybe I shouldn't trust my vision on this, but the samples don't really look uniformly spread within the simplex. For some reason they only seem to do for a triangle.

Any thoughts?

Thanks

Christophe