[SciPy-dev] ticket 416 (gradient in leastsq): some questions

dmitrey openopt@ukr....
Thu Jul 26 06:44:30 CDT 2007


Hi all,
the 416th ticket was ascribed to me.

There some problems:

1. What's the difference between these 2 funcs from __minpack.h:
int jac_multipack_calling_function(int *n, double *x, double *fvec, 
double *fjac, int *ldfjac, int *iflag)
int jac_multipack_lm_function(int *m, int *n, double *x, double *fvec, 
double *fjac, int *ldfjac, int *iflag)

They have same description.

  /* This is the function called from the Fortran code it should
        -- use call_python_function to get a multiarrayobject result
    -- check for errors and return -1 if any
    -- otherwise place result of calculation in *fvec or *fjac.

     If iflag = 1 this should compute the function.
     If iflag = 2 this should compute the jacobian (derivative matrix)
  */

2. So patch assigned to the ticket proposes to rewrite the line 152
MATRIXC2F(fjac, result_array->data, *n, *ldfjac)
as
MATRIXC2F(fjac, result_array->data, *ldfjac, *n)

however, line 92 is the same. so maybe it needs same patch.

3. The MATRIXC2F is defined in minpack.h w/o any description:
#define MATRIXC2F(jac,data,n,m) {double *p1=(double *)(jac), *p2, 
*p3=(double *)(data);\
int i,j;\
for (j=0;j<(m);p3++,j++) \
  for (p2=p3,i=0;i<(n);p2+=(m),i++,p1++) \
    *p1 = *p2; }

I have no idea what does it do.
So I replaced (jac,data,n,m) by (jac,data,m,n), and user's example works 
correctly for all cases 1-3:
1) w/o gradient info
2) with gradient info, col_deriv=0
3) with gradient info, col_deriv=1 (in the case I modified the user's 
gradient func so that it returns transposed gradient)

scipy.test(1) also didn't yield any bugs related to leastsq.

Do you agree to submit the changes to svn?

Regards, D.


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