bool AmplTMINLP::eval_grad_gi(Index n, const Number* x, bool new_x,
Index i, Index& nele_grad_gi, Index* jCol,
Number* values)
{
ASL_pfgh* asl = ampl_tnlp_->AmplSolverObject();
if (jCol) {
// Only compute the number of nonzeros and the indices
DBG_ASSERT(!values);
nele_grad_gi = 0;
for (cgrad* cg=Cgrad[i]; cg; cg = cg->next) {
jCol[nele_grad_gi++] = cg->varno + 1;
}
return true;
}
DBG_ASSERT(values);
// ignore new_x for now
xunknown();
asl->i.congrd_mode = 1;
fint nerror = 0;
congrd(i, const_cast<real*>(x), values, &nerror);
if (nerror!=0) {
return false;
}
else {
return true;
}
}
//can be called if hessian is returned in triangular form, otherwise more works need to be done as the get_matrices routine
void
ampl_GetHessianInit(double *varsX, double *Helts,double *Yelts)
{
OW[0] = 1;
if(varsX==NULL)
xunknown();
if(Yelts)
sphes(Helts, -1, OW, Yelts);
else{
double *tempY = (double*)malloc(n_con*sizeof(double));
sphes(Helts, -1, OW, tempY);
delete tempY;
}
}
bool AmplTMINLP::eval_gi(Index n, const Number* x, bool new_x,
Index i, Number& gi)
{
ASL_pfgh* asl = ampl_tnlp_->AmplSolverObject();
// ignore new_x for now
xunknown();
fint nerror = 0;
gi = conival(i, const_cast<real*>(x), &nerror);
if (nerror!=0) {
return false;
}
else {
return true;
}
}
