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List:       scilab-dev
Subject:    SciFunction
From:       COLLETTE Yann <yann.collette () renault ! com>
Date:       2008-02-06 13:50:30
Message-ID: 47A9BB26.70501 () renault ! com
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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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