vectorizing calculation of eigen values of a large multi-dimensional array
Afficher commentaires plus anciens
Dear All,
I have a 3D rectangular domain. Each point is associated with a vector(u,v,w) through time. u, v, and w are of size nx×ny×nz×nt, which represents the resolution of the domain in x, y, z, and time. I want to calculate eigen values of a tensor which is obtained from the vector field at each point and for all the times in a vectorized fashion. It is very easy to use for-loop over time and position but u, v, w are large. The following is just a working example.
nx = 10; ny = 10; nz = 10; nt = 5;
u = ones(nx, ny, nz, nt);
v = ones(nx, ny, nz, nt);
w = ones(nx, ny, nz, nt);
all_eigen_vals = zeros(nx,ny,nz,nt,3);
for t=1:nt
for k=1:nz
for j=1:ny
for i=1:nx
tensor=[u(i,j,k,t)^2, u(i,j,k,t)*v(i,j,k,t), u(i,j,k,t)*w(i,j,k,t);
u(i,j,k,t)*v(i,j,k,t), v(i,j,k,t)^2, v(i,j,k,t)*w(i,j,k,t);
u(i,j,k,t)*w(i,j,k,t), v(i,j,k,t)*w(i,j,k,t), w(i,j,k,t)^2]
all_eigen_vals(i,j,k,t,:) = eig(tensor);
end
end
end
end
Could someone help me?
Réponse acceptée
Plus de réponses (1)
Richard Brown
le 7 Août 2013
Modifié(e) : Richard Brown
le 7 Août 2013
You'll get a bit of a performance boost simply by looping over linear indices rather than nesting (this is more than twice as fast on my system):
evals = zeros(3, numel(u));
for i = 1:numel(u)
tensor=[u(i)^2, u(i)*v(i), u(i)*w(i);
u(i)*v(i), v(i)^2, v(i)*w(i);
u(i)*w(i), v(i)*w(i), w(i)^2];
evals(:,i) = eig(tensor);
end
evals = reshape(evals,3,nx,ny,nz,nt);
note that I've put changed the order of indexing in the evals matrix so that each set of 3 eigenvalues ought to be contiguous in memory. You'll probably want to double check the right bits are going in the right places.
Catégories
En savoir plus sur Linear Algebra dans Centre d'aide et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!