SciFunction
COLLETTE Yann
yann.collette at renault.com
Wed Feb 6 14:50:30 CET 2008
Hello,
I'm trying to interface an optimisation method under scilab (the
optimization method is in fortran and I'm writing the interface in C).
My question is: how to retrieve the output parameters of a call to
SciFunction ?
The following program is called several times during the run of the
optimization:
/////////////////////////////////////
// First I create some parameters. The first one will contain the "x"
vector and the second one the gradient of the output (I do this to give
the correct size to the stack. If I don't do this, scilab says it
doesn't know the ibegin + 1 parameter).
// Create the first variable: x
n_x1_tmp = n_x;
m_x1_tmp = m_x;
CreateVar(ibegin,"d",&n_x1_tmp,&m_x1_tmp,&l_x1_tmp);
for(i=0;i<ndv; i++) *dstk(l_x1_tmp+i) = *dstk(l_x+i);
// Create a fake 2nd variable on the stack. This will allow to call
GetRhsVar to retrieve the 2nd output argument
n_x2_tmp = 1;
m_x2_tmp = 1;
CreateVar(ibegin+1,"d",&n_x2_tmp,&m_x2_tmp,&l_x2_tmp);
///////////////////////////////////
// I call the scilab function via SciFunction
// Call to the scilab function
SciFunction(&ibegin,&l_fobj,&fobj_lhs,&fobj_rhs);
if (Err>0)
{
sciprint("conmin: error when calling objective function\n");
return 0;
} /* End If */
n1 = ndv + 2;
////////////////////////////////////
// Now I get the parameters via GetRhsVar. I hope that the size of the
ibegin and ibegin+1 parameters are set correctly by SciFunction (first
free the memory and then mallocated correctly to get the output parameters).
// Get fobj
GetRhsVar(ibegin, "d",&n_fobj_tmp, &m_fobj_tmp, &l_fobj_tmp);
// Get gradient of fobj
GetRhsVar(ibegin+1,"d",&n_dfobj_tmp,&m_dfobj_tmp,&l_dfobj_tmp);
/////////////////////////////////////
// Finally, I store all the output parameters in temporary arrays
(because just after the objective function, I called the constraints
also via SciFunction).
// Fill C2F(cnmn1).obj: objective function value
C2F(cnmn1).obj = *dstk(l_fobj_tmp);
// DF(N1) Analytic gradient of the objective function for the current
decision variables, X(I). DF(I) contains the partial derivative
// of OBJ with respect to X(I). Calculate DF(I), I = 1, NDV if
INFO = 3 or INFO = 4 and if NFDG = 0 or NFDG = 2.
double * DF = (double *)malloc(n1*sizeof(double));
// Fill DF(N1): gradient of objective function value
for(j=0;j<ndv;j++) DF[j] = *dstk(l_dfobj_tmp+j);
///////////////////////////////////////
// Is my strategy good or do you recommend another method ?
Your sincerely,
Yann COLLETTE
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