|
| using | vector_type = Vector< double > |
| |
| | SolverBase (SolverControl &solver_control, VectorMemory< Vector< double > > &vector_memory) |
| |
| | SolverBase (SolverControl &solver_control) |
| |
| boost::signals2::connection | connect (const std::function< SolverControl::State(const unsigned int iteration, const double check_value, const Vector< double > ¤t_iterate)> &slot) |
| |
| | Subscriptor () |
| |
| | Subscriptor (const Subscriptor &) |
| |
| | Subscriptor (Subscriptor &&) noexcept |
| |
| virtual | ~Subscriptor () |
| |
| Subscriptor & | operator= (const Subscriptor &) |
| |
| Subscriptor & | operator= (Subscriptor &&) noexcept |
| |
| void | subscribe (std::atomic< bool > *const validity, const std::string &identifier="") const |
| |
| void | unsubscribe (std::atomic< bool > *const validity, const std::string &identifier="") const |
| |
| unsigned int | n_subscriptions () const |
| |
| template<typename StreamType > |
| void | list_subscribers (StreamType &stream) const |
| |
| void | list_subscribers () const |
| |
| template<class Archive > |
| void | serialize (Archive &ar, const unsigned int version) |
| |
| static ::ExceptionBase & | ExcInUse (int arg1, std::string arg2, std::string arg3) |
| |
| static ::ExceptionBase & | ExcNoSubscriber (std::string arg1, std::string arg2) |
| |
| GrowingVectorMemory< Vector< double > > | static_vector_memory |
| |
| VectorMemory< Vector< double > > & | memory |
| |
| boost::signals2::signal< SolverControl::State(const unsigned int iteration, const double check_value, const Vector< double > ¤t_iterate), StateCombiner > | iteration_status |
| |
template<typename VectorType = Vector<double>>
class EigenPower< VectorType >
Power method (von Mises) for eigenvalue computations.
This method determines the largest eigenvalue of a matrix by applying increasing powers of this matrix to a vector. If there is an eigenvalue \(l\) with dominant absolute value, the iteration vectors will become aligned to its eigenspace and \(Ax = lx\).
A shift parameter allows to shift the spectrum, so it is possible to compute the smallest eigenvalue, too.
Convergence of this method is known to be slow.
- Author
- Guido Kanschat, 2000
Definition at line 55 of file eigen.h.
template<class VectorType >
template<typename MatrixType >
Power method. x is the (not necessarily normalized, but nonzero) start vector for the power method. After the iteration, value is the approximated eigenvalue and x is the corresponding eigenvector, normalized with respect to the l2-norm.
Definition at line 210 of file eigen.h.