void SNLLBase::
copy_con_grad(const RealMatrix& local_fn_grads, RealMatrix& grad_g,
const size_t& offset)
{
// Unlike DAKOTA, OPT++ expects nonlinear equations followed by nonlinear
// inequalities. Therefore, we have to reorder the constraint gradients.
// Assign the gradients of the nonlinear constraints. Let g: R^n -> R^m.
// The gradient of g, grad_g, is the n x m matrix whose ith column is the
// gradient of g_i. The transpose of grad_g is called the Jacobian of g.
// WJB: candidate for more efficient rewrite?? (both are ColumnMajor grads!)
size_t i, j, n = local_fn_grads.numRows(),
num_nln_eq_con = optLSqInstance->numNonlinearEqConstraints,
num_nln_ineq_con = optLSqInstance->numNonlinearIneqConstraints;
for (i=0; i<n; i++)
for (j=0; j<num_nln_eq_con; j++)
grad_g(i, j) = local_fn_grads(i,offset+num_nln_ineq_con+j);
for (i=0; i<n; i++)
for (j=0; j<num_nln_ineq_con; j++)
grad_g(i, j+num_nln_eq_con) = local_fn_grads(i,offset+j);
}
