// Minimization function (exact)
auto ls_minimization_function =
- [this,
- &residual_0,
- &solution_delta,
- &newton_update,
- &tang_mtrx_norm_old](const double ss /*step size*/) {
+ [this, &solution_delta, &newton_update, &tang_mtrx_norm_old](
+ const double ss /*step size*/) {
// Ensure that the constraints for the Dirichlet BC's are correct,
// irrespective of the chosen step size.
BlockVector<double> solution_delta_trial(newton_update);
BlockVector<double> residual_trial = this->system_rhs;
residual_trial *= -1.0; // Residual = -RHS
-
const double tang_mtrx_norm_new =
tangent_matrix.block(0, 0).frobenius_norm();
+ (void)tang_mtrx_norm_new;
if (ss != 0.0)
Assert(tang_mtrx_norm_new != tang_mtrx_norm_old,
ExcInternalError());
public:
Real
- value(const ROL::Vector<Real> &x, Real &tol)
+ value(const ROL::Vector<Real> &x, Real & /*tol*/)
{
Assert(x.dimension() == 2, ExcInternalError());
}
void
- gradient(ROL::Vector<Real> &g, const ROL::Vector<Real> &x, Real &tol)
+ gradient(ROL::Vector<Real> &g, const ROL::Vector<Real> &x, Real & /*tol*/)
{
Teuchos::RCP<const VectorType> xp = this->get_rcp_to_VectorType(x);
Teuchos::RCP<VectorType> gp = this->get_rcp_to_VectorType(g);
}
int
-main(int argc, char **argv)
+main()
{
try
{