Convolution using non-constant kernel?
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Is there a faster way of performing convolution using a non-constant kernel than with a loop? The kernel contains values completely independent from those in the first vector.
I have been able to achieve what I need by code similar to what follows, but it is being run millions of times and I would love to optimize it, if possible.
rng(42);
nr = 100000;
A = rand(nr,1);
B = rand(nr,1);
w = 100; % window length
D = NaN(nr,1); % initialize results vector
for i = w:nr
D(i) = A((i-w+1):i)' * B((i-w+1):i);
end
The following was added on 07 Oct 2023:
This small example illustrates that the kernel of convolution, represented here by B, changes along with the sliding window.
nr = 10;
A = [5, 3, 2, 7, 8, 6, 4, 5, 8, 6];
B = [1, 2, 3, 2, 3, 2, 1, 2, 2, 3];
w = 3; % window length
D = NaN(1, nr); % initialize results vector
for i = w:nr
D(i) = A((i-w+1):i) * B((i-w+1):i)'; % values in B change for each i
end
disp(D)
This results in the following:
D = [NaN, NaN, 17, 26, 44, 50, 40, 26, 30, 44]
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Accepted Answer
Paul
on 6 Apr 2024
Edited: Paul
on 6 Apr 2024
Hi goc3,
filter provides significant speedup
rng(42);
nr = 100000;
A = rand(nr,1);
B = rand(nr,1);
w = 100; % window length
tic
for count = 1:100
D = NaN(nr,1); % initialize results vector
for i = w:nr
D(i) = A((i-w+1):i)' * B((i-w+1):i);
end
end
toc
tic
for count = 1:100
% assumes A is real (original code uses conjugate transpose)
D1 = filter(ones(1,w),1,A.*B,nan(1,w-1));
end
toc
isequal(isnan(D),isnan(D1))
plot(abs(D-D1))
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More Answers (1)
Shivansh
on 5 Oct 2023
Hi goc3,
Yes, there is a faster way to perform convolution using a non-constant kernel without using a loop. You can use the conv() function in MATLAB to achieve this. Here's an example of how you can modify your code to utilize conv:
rng(42);
nr = 100000;
A = rand(nr,1);
B = rand(nr,1);
w = 100; % window length
kernel = B(w:-1:1); % Reverse the kernel to match the order used in the loop
% Perform convolution using the 'valid' option to match the loop implementation
D = conv(A, kernel, 'valid');
In this code, we create a non-constant kernel as a vector kernel. You can replace it with your actual kernel values. Then, we use the conv() function with the 'valid' option, which computes only the valid part of the convolution and discards any overlapping edges. This is equivalent to the operation you were performing in your loop.
Using conv should be significantly faster than using a loop for large arrays, as it takes advantage of optimized algorithms implemented in MATLAB. For more information on ‘conv’, you can refer to the documentation here https://in.mathworks.com/help/matlab/ref/conv.html?searchHighlight=conv&s_tid=srchtitle_support_results_1_conv.
Hope it helps!
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