nonlin_curvefit - optim_doc
Description: optim_doc

Next: lm_svd_feasible, Previous: nonlin_residmin, Up: Residual optimization

### 2.2 Function nonlin_curvefit() for curve fitting

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`.

— Function File: [p, fy, cvg, outp] = nonlin_curvefit (f, pin, x, y)
— Function File: [p, fy, cvg, outp] = nonlin_curvefit (f, pin, x, y, settings)

> 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.