Custom jacobian (dfdp) in nonlin_curvefit - calling parameters?

[Pavel Hofman]

Hi,

I am trying to find out, if providing custom jacobian results in faster 
execution than the default real finite differences. However, I got stuck 
at defining the custom dfdp function. Please is there any example which 
involves multiple independent values?

My case is fitting sum of two sine functions at vector of times t to 
observed values y. The problem is I do not know how the custom dfdp 
function should look like, with what params it is being called. In my 
code below my dfdp (f_dfdp) gets called with row of times (t) which I 
cannot convert to the jacobian matrix. The dfdp description in 
nonlin_residmin talks about some structures, but I was not able to find 
any example.

As of the unit test in nonlin_curfit.m (chittkowski_327) - I was able to 
find the commit diff but this case again looks like a single independent 
value is being used.

I very much appreciate any help, thanks a lot.

Best regards,

Pavel.


t = 0:100;

function y = f_calc(f1, a1, s1, f2, a2, s2, t)
  persistent PI2 = 2 * pi;

  y = a1 * cos(PI2 * f1 * t + s1) + a2 * cos(PI2 * f2 * t + s2);
endfunction

function J = f_dfdp(f1, a1, s1, f2, a2, s2, t)
  persistent PI2 = 2 * pi;

    J = [-PI2 * a1 * t * sin(PI2 * f1  * t + s1) , cos(PI2 * f1 * t + 
s1) ,  -a1  * sin(PI2 * f1 * t + s1), -PI2 * a2 * t * sin(PI2 * f2 * t + 
s2),  cos(PI2 * f2 * t + s2), -a2 * sin(PI2 * f2 * t + s2)];
endfunction


f = @(p, t) f_calc(p(1), p(2), p(3), p(4), p(5), p(6), t);

my_dfdp= @(p, t) f_dfdp(p(1), p(2), p(3), p(4), p(5), p(6), t);

settings = optimset ('dfdp', my_dfdp, 'Display','iter');


init = [measF1; measA1; measPhase1;measF2; measA2; measPhase2];


[p, model_values, cvg, outp] = nonlin_curvefit(f, init, t, y, settings);