SX SXFunctionInternal::hess(int iind, int oind) {
casadi_assert_message(output(oind).numel() == 1, "Function must be scalar");
SX g = grad(iind, oind);
g.makeDense();
if (verbose()) userOut() << "SXFunctionInternal::hess: calculating gradient done " << endl;
// Create function
Dict opts;
opts["verbose"] = getOption("verbose");
SXFunction gfcn("gfcn", make_vector(inputv_.at(iind)),
make_vector(g), opts);
// Calculate jacobian of gradient
if (verbose()) {
userOut() << "SXFunctionInternal::hess: calculating Jacobian " << endl;
}
SX ret = gfcn.jac(0, 0, false, true);
if (verbose()) {
userOut() << "SXFunctionInternal::hess: calculating Jacobian done" << endl;
}
// Return jacobian of the gradient
return ret;
}
USING_NAMESPACE_ACADO
void ffcn_model( double *x, double *f, void *user_data ){
int i;
double *xd = new double[NXD];
double *xa = new double[NXA];
double *u = new double[ NU];
double *p = new double[ NP];
for( i = 0; i < NXD; i++ ) xd[i] = x[ 1+i ];
for( i = 0; i < NXA; i++ ) xa[i] = x[ NXD+1+i ];
for( i = 0; i < NU; i++ ) u[i] = x[ NXA+NXD+1+i ];
for( i = 0; i < NP; i++ ) p[i] = x[ NXA+NXD+NU+1+i ];
ffcn( &x[0], xd, xa, u, p, f );
gfcn( &x[0], xd, xa, u, p, &(f[NXD]) );
delete[] xd;
delete[] xa;
delete[] u;
delete[] p;
}
