- 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`.Also, if the setting

`user_interaction`

is given, additional information is passed to these functions. Type`optim_doc ("Common optimization options")`

for this setting.**See also:**nonlin_residmin.

The following code

## Example for linear inequality constraints ## (see also the same example in 'demo nonlin_residmin') ## independents and observations indep = 1:5; obs = [1, 2, 4, 7, 14]; ## model function: f = @ (p, x) p(1) * exp (p(2) * x); ## initial values: init = [.25; .25]; ## linear constraints, A.' * parametervector + B >= 0 A = [1; -1]; B = 0; # p(1) >= p(2); settings = optimset ("inequc", {A, B}); ## start optimization [p, model_values, cvg, outp] = nonlin_curvefit (f, init, indep, obs, settings)

Produces the following output

p = 0.62034 0.62034 model_values = 1.1536 2.1451 3.9891 7.4179 13.7941 cvg = 3 outp = scalar structure containing the fields: user_interaction = scalar structure containing the fields: stop = [](0x1) info = {}(0x1) niter = 7 lambda = scalar structure containing the fields: lower = 0 0 upper = 0 0 eqlin = [](0x0) ineqlin = 269.24 eqnonlin = [](1x0) ineqnonlin = [](1x0)

Package: optim