Re: Custom jacobian (dfdp) in nonlin_curvefit - calling parameters?

[Pavel Hofman]

Hi Olaf,

> The interface of your functions and the way you call nonlin_curvefit
> seem ok. The code inside f_dfdp() doesn't seem ok. I don't think the
> documentation of the optim package contains an example for explicitly
> computing a Jacobian with m-code (because this is not specific to the
> optim package). But in the code of 'optim_problems.m' there is an
> example:
>
>    ret.curve.schittkowski_327.dfdp = ...
>        @ (x, p) [1 + exp(-p(2) * (x - 8)), ...
>                 (p(1) + .49) * (8 - x) .* exp(-p(2) * (x - 8))];
>
> Note that 'x' (or 't', in your code) is a column vector.

Thanks a lot for your help.

Function APIs of that example are:

ret.curve.schittkowski_327.f = @(x, p) ......
ret.curve.schittkowski_327.dfdp = @(x, p) ......

while nonlin_curvefit has @(p, x). Should the partial derivative dfdp 
function for nonlin_curvefit be @(x, p), or @(p, x)?

One more related question. In my other code I nonlin_curvefit 4 
parameters to two equations (each using all of the params), again for a 
row/vector of independent times t (i.e. x). As a result, my f(p, x) 
returns a 2D matrix. Works perfect. When providing a custom dfdp 
function, what dimensions should its output have? 2D + 1D for each p, 
i.e. 3D?

Thanks a lot,

Pavel.