GQCP::EigenproblemSolver Namespace Reference

Functions
template<typename Scalar >
Algorithm< EigenproblemEnvironment< Scalar > >	Dense ()
IterativeAlgorithm< EigenproblemEnvironment< double > >	Davidson (const size_t number_of_requested_eigenpairs=1, const size_t maximum_subspace_dimension=15, const double convergence_threshold=1.0e-08, double correction_threshold=1.0e-12, const size_t maximum_number_of_iterations=128, const double inclusion_threshold=1.0e-03)

Function Documentation

Davidson()

IterativeAlgorithm< EigenproblemEnvironment< double > > GQCP::EigenproblemSolver::Davidson	(	const size_t	number_of_requested_eigenpairs = 1,
		const size_t	maximum_subspace_dimension = 15,
		const double	convergence_threshold = 1.0e-08,
		double	correction_threshold = 1.0e-12,
		const size_t	maximum_number_of_iterations = 128,
		const double	inclusion_threshold = 1.0e-03
	)

Parameters

number_of_requested_eigenpairs	the number of solutions the Davidson solver should find
maximum_subspace_dimension	the maximum dimension of the subspace before collapsing
convergence_threshold	the threshold that is used in determining the norm on the residuals, which determines convergence
correction_threshold	the threshold used in solving the (approximated) residue correction equation
maximum_number_of_iterations	the maximum number of iterations the algorithm may perform
inclusion_threshold	the threshold on the norm used for determining if a new projected correction vector should be added to the subspace

Returns

an iterative algorithm that can find the lowest n eigenvectors of a matrix using Davidson's algorithm

Dense()

template<typename Scalar >

Algorithm< EigenproblemEnvironment< Scalar > > GQCP::EigenproblemSolver::Dense

(

)

Returns

An algorithm that can diagonalize a dense matrix.

Template Parameters

Scalar

The scalar type of matrix elements: real or complex.