Hello,
Generally, we do not disclose the inner workings of built-in MATLAB functions, which includes providing specific values for the Big-O complexity that can be expected.
To find the information about typical algorithms, you can try to empirically determine an estimate. The following example gives a reference where we determine the Big O complexity of the built in MATLAB function “eig()” :
T = []; N = 100:10:1000;
for n = N, disp(n)
A = rand(n);
tic; eig(A); t = toc; T = [T t];
end
figure; plot(log10(N),log10(T)); grid on;
As the figure window gets generated,
- Go to "Tools > Basic Fitting" and choose a linear fit.
- This will provide the equation with slope ‘a’ of the line, which is the exponent of n in the Big-O notation: t = O(n^a).
This code gives an example how the execution time of a built in MATLAB function “eig()” depends upon the input.
You can try to extend same sorts of experiments to CNN architecture to get a rough computation of Big O for the same.
