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Hi, we have used scipy cobyla minimaziation successfully to minimize square error of a parameterized function to data points while obeying the constrain that "function - datapoint" is strictly non-negative. "function" could be anything that can be given by a python function (you might need scipy.vectorize) !! It worked grate. Just used from scipy.omtmize import cobyla (this is from memory - but should be close) and follow the example in the source code. Sebastian Haase Robert Kern wrote:
Clinton Allen wrote:
You might look at Chebyshev polynomials. They have a "min-max of the error" property. One place to start is http://mathworld.wolfram.com/ChebyshevApproximationFormula.html <http://mathworld.wolfram.com/ChebyshevApproximationFormula.html>
Unfortunately, that's two-sided error. It won't do what he wants.
Don't know if there's any python code for using these.
scipy.special.cheby{t,u,c,s}