vectorization of for loop
显示 更早的评论
Hi,
I made a 2d matrix with two for loops:
for k = 1:32
for l = 1:32
P_new(l,k) = P_old(l) + (LODF(l,k) * P_old(k));
end
end
P_old is here a 32 x 1 matrix and LODF is a 32 x 32 matrix which is already computed. How can I vectorize this code to avoid the for loops? Thanks in advance.
采纳的回答
Matt J
2013-12-10
P_new= bsxfun(@times, LODF, P_old.');
P_new= bsxfun(@plus, P_new,P_old);
2 个评论
For 32x32 data, I wouldn't be surprised if the for-loop was the fastest approach. For larger sizes, however, the vectorized approach will start to show superior performance, e.g.,
N=3200;
LODF=rand(N); P_old=rand(N,1);
tic;
P_new=zeros(size(LODF));
for k = 1:N
for l = 1:N
P_new(l,k) = P_old(l) + (LODF(l,k) * P_old(k));
end
end
toc;
%Elapsed time is 0.102201 seconds.
tic;
P_new= bsxfun(@times, LODF, P_old.');
P_new= bsxfun(@plus, P_new,P_old);
toc
%Elapsed time is 0.043591 seconds.
If you're not happy with the speed of your code, you should show us the slow part in its entirety. The small part you've shown is pretty fast, in and of itself.
更多回答（2 个）
Jos (10584)
2013-12-10
for loops are pretty fast when you use pre-allocation
P_new = zeros(32,32) ;
for k = 1:32
for l = 1:32
P_new(l,k) = P_old(l) + (LODF(l,k) * P_old(k));
end
end
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