#include <GenEigsRealShiftSolver.h>

Inheritance diagram for Spectra::GenEigsRealShiftSolver< Scalar, SelectionRule, OpType >:

Public Member Functions
	GenEigsRealShiftSolver (OpType *op, Index nev, Index ncv, Scalar sigma)

Public Member Functions inherited from Spectra::GenEigsBase< Scalar, SelectionRule, OpType, IdentityBOp >
Index	compute (Index maxit=1000, Scalar tol=1e-10, int sort_rule=LARGEST_MAGN)

ComplexVector	eigenvalues () const

ComplexMatrix	eigenvectors (Index nvec) const

ComplexMatrix	eigenvectors () const

int	info () const

void	init (const Scalar *init_resid)

void	init ()

Index	num_iterations () const

Index	num_operations () const

Private Types
typedef std::complex< Scalar >	Complex

typedef Eigen::Array< Complex, Eigen::Dynamic, 1 >	ComplexArray

typedef Eigen::Index	Index

Private Member Functions
void	sort_ritzpair (int sort_rule)

Private Attributes
const Scalar	m_sigma

Additional Inherited Members
Protected Attributes inherited from Spectra::GenEigsBase< Scalar, SelectionRule, OpType, IdentityBOp >
ArnoldiFac	m_fac

const Index	m_n

const Index	m_ncv

const Index	m_nev

Index	m_niter

Index	m_nmatop

OpType *	m_op

ComplexVector	m_ritz_est

ComplexVector	m_ritz_val

ComplexMatrix	m_ritz_vec

Detailed Description

template<typename Scalar = double, int SelectionRule = LARGEST_MAGN, typename OpType = DenseGenRealShiftSolve<double>>
class Spectra::GenEigsRealShiftSolver< Scalar, SelectionRule, OpType >

This class implements the eigen solver for general real matrices with a real shift value in the shift-and-invert mode. The background knowledge of the shift-and-invert mode can be found in the documentation of the SymEigsShiftSolver class.

Template Parameters

Scalar	The element type of the matrix. Currently supported types are `float`, `double` and `long double`.
SelectionRule	An enumeration value indicating the selection rule of the shifted-and-inverted eigenvalues. The full list of enumeration values can be found in Enumerations.
OpType	The name of the matrix operation class. Users could either use the wrapper classes such as DenseGenRealShiftSolve and SparseGenRealShiftSolve, or define their own that implements all the public member functions as in DenseGenRealShiftSolve.

Definition at line 41 of file GenEigsRealShiftSolver.h.

Member Typedef Documentation

◆ Complex

template<typename Scalar = double, int SelectionRule = LARGEST_MAGN, typename OpType = DenseGenRealShiftSolve<double>>

typedef std::complex<Scalar> Spectra::GenEigsRealShiftSolver< Scalar, SelectionRule, OpType >::Complex

private

Definition at line 45 of file GenEigsRealShiftSolver.h.

◆ ComplexArray

template<typename Scalar = double, int SelectionRule = LARGEST_MAGN, typename OpType = DenseGenRealShiftSolve<double>>

typedef Eigen::Array<Complex, Eigen::Dynamic, 1> Spectra::GenEigsRealShiftSolver< Scalar, SelectionRule, OpType >::ComplexArray

private

Definition at line 46 of file GenEigsRealShiftSolver.h.

◆ Index

template<typename Scalar = double, int SelectionRule = LARGEST_MAGN, typename OpType = DenseGenRealShiftSolve<double>>

typedef Eigen::Index Spectra::GenEigsRealShiftSolver< Scalar, SelectionRule, OpType >::Index

private

Definition at line 44 of file GenEigsRealShiftSolver.h.

Constructor & Destructor Documentation

◆ GenEigsRealShiftSolver()

template<typename Scalar = double, int SelectionRule = LARGEST_MAGN, typename OpType = DenseGenRealShiftSolve<double>>

Spectra::GenEigsRealShiftSolver< Scalar, SelectionRule, OpType >::GenEigsRealShiftSolver	(	OpType *	op,
		Index	nev,
		Index	ncv,
		Scalar	sigma
	)

inline

Constructor to create a eigen solver object using the shift-and-invert mode.

Parameters

op	Pointer to the matrix operation object. This class should implement the shift-solve operation of : calculating $(A-\sigma I)^{-1}v$ for any vector . Users could either create the object from the wrapper class such as DenseGenRealShiftSolve, or define their own that implements all the public member functions as in DenseGenRealShiftSolve.
nev	Number of eigenvalues requested. This should satisfy $1\le nev \le n-2$ , where is the size of matrix.
ncv	Parameter that controls the convergence speed of the algorithm. Typically a larger `ncv` means faster convergence, but it may also result in greater memory use and more matrix operations in each iteration. This parameter must satisfy $nev+2 \le ncv \le n$ , and is advised to take $ncv \ge 2\cdot nev + 1$ .
sigma	The real-valued shift.