In curve fitting, the model function computes values from a constant set
of `independents', and the intention is to minimize the differences of
these computed values to a constant set of `observations'. This can be
done with nonlin_residmin
, but it is more convenient to use
nonlin_curvefit
, which cares for passing the constant
`independents' to the model function and for calculating the differences
to the constant `observations'.
However, if in some optimization problem you notice that you end up with
passing dummy-values for the `independents' and zeros for the
`observations', you can more naturally use nonlin_residmin
instead of nonlin_curvefit
.
Frontend for nonlinear fitting of values, computed by a model function, to observed values.
Please refer to the description of
nonlin_residmin
. The differences tononlin_residmin
are the additional arguments x (independent values, mostly, but not necessarily, an array of the same dimensions or the same number of rows as y) and y (array of observations), the returned value fy (final guess for observed values) instead of resid, that the model function has a second obligatory argument which will be set to x and is supposed to return guesses for the observations (with the same dimensions), and that the possibly user-supplied function for the jacobian of the model function has also a second obligatory argument which will be set to x.See also: nonlin_residmin.
Also, if the setting user_interaction
is given, additional
information is passed to these functions, see