[SciPy-User] ODR , Levenberg-Marquardt, non linear fitting and convergence: some assistance needed
Wed Apr 28 11:10:02 CDT 2010
On Mon, Apr 26, 2010 at 1:09 PM, ms <email@example.com> wrote:
> I am currently smashing my head on the following problem.
> I am trying to fit data to two equations, which are two levels of
> approximation for the same model. I am currently using ODR to fit.
> Of course the 2nd order approximation is mathematically bit more
> complicated than the first, involving a long summatory etc. but the
> resulting curve and behaviour are overall very similar.
> Now, in my tests, the 1st order approx. usually converges, while the 2nd
> order does not converge at all: not that it gives some wrong result, it
> remains stuck to the initial parameters with zero values in the
> covariance, etc. This even when I feed to ODR starting values very close
> to the "true" ones.
> I used fmin so far to bypass this problem, but it is really slow.
> Recently a collegue of mine told me that he can get Levenberg-Marquardt
> to minimize the untreatable (in my system) 2nd order approx, using
> Mathematica, out of the box.
> I have no idea unfortunately what are the differences between the
> Mathematica and the ODRPACK implementations of Levenberg-Marquardt, but
> if one can do it I think the other one can too. So, what should I try to
> improve my system? I tried increasing iterations, fixing X values etc.
> but nothing seems to work properly. Do you have any hint?
scipy.optimize.leastsq , and
scipy.optimize.curve_fit (scipy trunk)
worth a try
> Thanks a lot!
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