#include <GenEigsSolver.h>

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

Public Member Functions
	GenEigsSolver (OpType *op, Index nev, Index ncv)

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 Eigen::Index	Index

Additional Inherited Members
Protected Member Functions inherited from Spectra::GenEigsBase< Scalar, SelectionRule, OpType, IdentityBOp >
virtual void	sort_ritzpair (int sort_rule)

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 = DenseGenMatProd<double>>
class Spectra::GenEigsSolver< Scalar, SelectionRule, OpType >

This class implements the eigen solver for general real matrices, i.e., to solve $Ax=\lambda x$ for a possibly non-symmetric $A$ matrix.

Most of the background information documented in the SymEigsSolver class also applies to the GenEigsSolver class here, except that the eigenvalues and eigenvectors of a general matrix can now be complex-valued.

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 requested eigenvalues, for example `LARGEST_MAGN` to retrieve eigenvalues with the largest magnitude. 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 DenseGenMatProd and SparseGenMatProd, or define their own that implements all the public member functions as in DenseGenMatProd.

An example that illustrates the usage of GenEigsSolver is give below:

#include <Eigen/Core>
#include <Spectra/GenEigsSolver.h>
// <Spectra/MatOp/DenseGenMatProd.h> is implicitly included
#include <iostream>
using namespace Spectra;
int main()
{
    // We are going to calculate the eigenvalues of M
    Eigen::MatrixXd M = Eigen::MatrixXd::Random(10, 10);
    // Construct matrix operation object using the wrapper class
    DenseGenMatProd<double> op(M);
    // Construct eigen solver object, requesting the largest
    // (in magnitude, or norm) three eigenvalues
    GenEigsSolver< double, LARGEST_MAGN, DenseGenMatProd<double> > eigs(&op, 3, 6);
    // Initialize and compute
    eigs.init();
    int nconv = eigs.compute();
    // Retrieve results
    Eigen::VectorXcd evalues;
    if(eigs.info() == SUCCESSFUL)
        evalues = eigs.eigenvalues();
    std::cout << "Eigenvalues found:\n" << evalues << std::endl;
    return 0;
}

And also an example for sparse matrices:

#include <Eigen/Core>
#include <Eigen/SparseCore>
#include <Spectra/GenEigsSolver.h>
#include <Spectra/MatOp/SparseGenMatProd.h>
#include <iostream>
using namespace Spectra;
int main()
{
    // A band matrix with 1 on the main diagonal, 2 on the below-main subdiagonal,
    // and 3 on the above-main subdiagonal
    const int n = 10;
    Eigen::SparseMatrix<double> M(n, n);
    M.reserve(Eigen::VectorXi::Constant(n, 3));
    for(int i = 0; i < n; i++)
    {
        M.insert(i, i) = 1.0;
        if(i > 0)
            M.insert(i - 1, i) = 3.0;
        if(i < n - 1)
            M.insert(i + 1, i) = 2.0;
    }
    // Construct matrix operation object using the wrapper class SparseGenMatProd
    SparseGenMatProd<double> op(M);
    // Construct eigen solver object, requesting the largest three eigenvalues
    GenEigsSolver< double, LARGEST_MAGN, SparseGenMatProd<double> > eigs(&op, 3, 6);
    // Initialize and compute
    eigs.init();
    int nconv = eigs.compute();
    // Retrieve results
    Eigen::VectorXcd evalues;
    if(eigs.info() == SUCCESSFUL)
        evalues = eigs.eigenvalues();
    std::cout << "Eigenvalues found:\n" << evalues << std::endl;
    return 0;
}

Definition at line 128 of file GenEigsSolver.h.

Member Typedef Documentation

◆ Index

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

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

private

Definition at line 131 of file GenEigsSolver.h.

Constructor & Destructor Documentation

◆ GenEigsSolver()

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

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

inline

Constructor to create a solver object.

Parameters

op	Pointer to the matrix operation object, which should implement the matrix-vector multiplication operation of : calculating for any vector . Users could either create the object from the wrapper class such as DenseGenMatProd, or define their own that implements all the public member functions as in DenseGenMatProd.
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 $ncv \ge 2\cdot nev + 1$ .

Definition at line 151 of file GenEigsSolver.h.

The documentation for this class was generated from the following file:

GenEigsSolver.h

Public Member Functions

Private Types