Next: lm_svd_feasible, Previous: nonlin_residmin, Up: Residual optimization [Index]
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 to nonlin_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
Common optimization options.
Next: lm_svd_feasible, Previous: nonlin_residmin, Up: Residual optimization [Index]