Does anyone know how to program a mathematica algorithm that does the same thing that FindFit does? Is there documentation of it somewhere? I assume it is a least squares algorithm but with very generalized arguments. I ask because I'm trying to use 'weighted' least squares and it basically involves just one more factor.

  • 1
    $\begingroup$ NonlinearModelFit[] can handle weighted nonlinear least squares. $\endgroup$ Oct 31, 2015 at 14:04

1 Answer 1


Fit works using singular value decomposition. FindFit uses the same method for the linear least-squares case, the Levenberg–Marquardt method for nonlinear least-squares, and general FindMinimum methods for other norms.

- source

NonlinearModelFit allows fitting of weighted data, as J.M. commented


The best fit parameters are a property of the model:

p = Table[Prime[x], {x, 20}];

nlm = NonlinearModelFit[p, a x Log[b + c x], {a, b, c}, x];
{a -> 1.42076, b -> 1.65558, c -> 0.534645}
  • $\begingroup$ How do I get the list parameters from NonlinearModel? FindFit outputs such a thing $\endgroup$ Oct 31, 2015 at 16:56
  • $\begingroup$ @minusatwelfth see my edit. $\endgroup$
    – paw
    Oct 31, 2015 at 17:12
  • $\begingroup$ @minus, it's all in the docs, if you'd look for them. $\endgroup$ Nov 1, 2015 at 2:04

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