RMS最小化加速

Question

[Environment: MATLAB 64 bit, Windows 7, Intel I5-2320]

I would like to RMS-fit a function to experimental data y , so I am minimizing the following function (by using fminsearch ):

minfunc = rms(y - fitfunc)

From the general point of view, does it make sense to minimize:

minfunc = sum((y - fitfunc) .^ 2) 

instead and then (after minimization) just do minfunc = sqrt(minfunc / N) to get the fit RMS error?

To reformulate the question, how much time (roughly, in percent) would fminsearch save by not doing sqrt and 1/(N - 1) each time? I wouldn't like to decrease readability of my code if my CPU / MATLAB are so fast that it wouldn't improve performance by at least a percent.

Update: I've tried simple tests, but the results are not clear: depending on the actual value of the minfunc , fminsearch takes more or less time.

Answer 1

The general answer for performance questions:

If you just want to figure out what is faster, design a benchmark and run it a few times.

By just providing general information it is not likely that you will determine which method is 1 percent faster.