Efficient way to multiply a vector of size (Nx,1) with a matrix of size (Nx+1,Nx+1)
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Matthew Kehoe
el 28 de Jul. de 2021
Comentada: the cyclist
el 29 de Jul. de 2021
My code repeatedly computes a vector of size (Nx,1) by a matrix of size (Nx+1,Nx+1):
clc; clear all;
Nx = 32;
a = 1.5;
b = 2;
gamma = rand(Nx,1);
D = rand(Nx+1,Nx+1);
D2 = D*D;
identy = eye(Nx+1,Nx+1);
% Computation performed by my code millions of times
for j=1:Nx
A = a*D2 + b*D + gamma(j)*identy;
% Can gamma(j)*identy be avoided through vectorization or a faster
% technique?
end
Is there an alternative way to compute A without using the for loop (would vectorization or repmat also work)?
2 comentarios
Matt J
el 28 de Jul. de 2021
In your example code, A does not evolve throughout the loop. Rather it is repeatedly over-written resulting in
A = a*D2 + b*D + gamma(Nx);
This is probably not what you want, but the reader cannot tell what it should really be doing.
Respuesta aceptada
the cyclist
el 28 de Jul. de 2021
If you permute gamma to be a vector along the dimension 3, then you can multiply it by identy, and the result will be a 33x33x32 array, where each "slice" along dimension 3 is the multiple with the corresponding value of gamma.
So, this calculation will do all of the calculations of your for loop (and store them all, rather than overwriting as your code does).
A = a*D2 + b*D + permute(gamma,[3,2,1]) .* identy;
You may now have a memory problem, though, if your arrays are large.
5 comentarios
the cyclist
el 29 de Jul. de 2021
That's fine, but if the question is self-contained, it might be better to open a new one, because it might get wider exposure.
You can always tag me as you did in your comment, and I will get a notification.
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