When to use cost function or error function for fitting

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Arbol 2017-6-26

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评论： Arbol 2017-6-26

I have look around different ways to fit. So when do you know you have to use the cost function: sum((realdata-fittingdata).^2) or error = realdata-fittingdata? For example, for lsqnonlin, you would use 'error' to find do the optimization. However, for fminsearch or patternsearch, you would use the 'cost function.'

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Answer 1

Alan Weiss 2017-6-26

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I'm sorry that you don't find the documentation clear on this issue.

If you are using lsqnonlin or lsqcurvefit, then your objective function should return the vector of function values (I mean fittingdata - realdata in your vocabulary).
If you are using any other nonlinear optimizer, then your objective function should be the sum of squares of the function values.