Eigen  3.4.90 (git rev 67eeba6e720c5745abc77ae6c92ce0a44aa7b7ae)
Eigen::GeneralizedEigenSolver< MatrixType_ > Class Template Reference

Detailed Description

template<typename MatrixType_> class Eigen::GeneralizedEigenSolver< MatrixType_ >

Computes the generalized eigenvalues and eigenvectors of a pair of general matrices.

This is defined in the Eigenvalues module.

#include <Eigen/Eigenvalues>
Template Parameters
 MatrixType_ the type of the matrices of which we are computing the eigen-decomposition; this is expected to be an instantiation of the Matrix class template. Currently, only real matrices are supported.

The generalized eigenvalues and eigenvectors of a matrix pair $$A$$ and $$B$$ are scalars $$\lambda$$ and vectors $$v$$ such that $$Av = \lambda Bv$$. If $$D$$ is a diagonal matrix with the eigenvalues on the diagonal, and $$V$$ is a matrix with the eigenvectors as its columns, then $$A V = B V D$$. The matrix $$V$$ is almost always invertible, in which case we have $$A = B V D V^{-1}$$. This is called the generalized eigen-decomposition.

The generalized eigenvalues and eigenvectors of a matrix pair may be complex, even when the matrices are real. Moreover, the generalized eigenvalue might be infinite if the matrix B is singular. To workaround this difficulty, the eigenvalues are provided as a pair of complex $$\alpha$$ and real $$\beta$$ such that: $$\lambda_i = \alpha_i / \beta_i$$. If $$\beta_i$$ is (nearly) zero, then one can consider the well defined left eigenvalue $$\mu = \beta_i / \alpha_i$$ such that: $$\mu_i A v_i = B v_i$$, or even $$\mu_i u_i^T A = u_i^T B$$ where $$u_i$$ is called the left eigenvector.

Call the function compute() to compute the generalized eigenvalues and eigenvectors of a given matrix pair. Alternatively, you can use the GeneralizedEigenSolver(const MatrixType&, const MatrixType&, bool) constructor which computes the eigenvalues and eigenvectors at construction time. Once the eigenvalue and eigenvectors are computed, they can be retrieved with the eigenvalues() and eigenvectors() functions.

Here is an usage example of this class: Example:

GeneralizedEigenSolver<MatrixXf> ges;
ges.compute(A, B);
cout << "The (complex) numerators of the generalzied eigenvalues are: " << ges.alphas().transpose() << endl;
cout << "The (real) denominatore of the generalzied eigenvalues are: " << ges.betas().transpose() << endl;
cout << "The (complex) generalzied eigenvalues are (alphas./beta): " << ges.eigenvalues().transpose() << endl;
static const RandomReturnType Random()
Definition: Random.h:114
Matrix< float, Dynamic, Dynamic > MatrixXf
Dynamic×Dynamic matrix of type float.
Definition: Matrix.h:500

Output:

The (complex) numerators of the generalzied eigenvalues are:  (-0.126,0.569) (-0.126,-0.569)      (-0.398,0)       (-1.12,0)
The (real) denominatore of the generalzied eigenvalues are: -1.56 -1.56 -1.25 0.746
The (complex) generalzied eigenvalues are (alphas./beta): (0.081,-0.365)  (0.081,0.365)     (0.318,-0)       (-1.5,0)


Public Types

typedef std::complex< RealScalar > ComplexScalar
Complex scalar type for MatrixType. More...

typedef Matrix< ComplexScalar, ColsAtCompileTime, 1, Options &~RowMajor, MaxColsAtCompileTime, 1 > ComplexVectorType
Type for vector of complex scalar values eigenvalues as returned by alphas(). More...

typedef CwiseBinaryOp< internal::scalar_quotient_op< ComplexScalar, Scalar >, ComplexVectorType, VectorTypeEigenvalueType
Expression type for the eigenvalues as returned by eigenvalues().

typedef Matrix< ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime > EigenvectorsType
Type for matrix of eigenvectors as returned by eigenvectors(). More...

typedef Eigen::Index Index

typedef MatrixType_ MatrixType
Synonym for the template parameter MatrixType_.

typedef MatrixType::Scalar Scalar
Scalar type for matrices of type MatrixType.

typedef Matrix< Scalar, ColsAtCompileTime, 1, Options &~RowMajor, MaxColsAtCompileTime, 1 > VectorType
Type for vector of real scalar values eigenvalues as returned by betas(). More...

Public Member Functions

const ComplexVectorTypealphas () const

const VectorTypebetas () const

GeneralizedEigenSolvercompute (const MatrixType &A, const MatrixType &B, bool computeEigenvectors=true)
Computes generalized eigendecomposition of given matrix. More...

EigenvalueType eigenvalues () const
Returns an expression of the computed generalized eigenvalues. More...

GeneralizedEigenSolver ()
Default constructor. More...

GeneralizedEigenSolver (const MatrixType &A, const MatrixType &B, bool computeEigenvectors=true)
Constructor; computes the generalized eigendecomposition of given matrix pair. More...

GeneralizedEigenSolver (Index size)
Default constructor with memory preallocation. More...

GeneralizedEigenSolversetMaxIterations (Index maxIters)

◆ ComplexScalar

template<typename MatrixType_ >
 typedef std::complex Eigen::GeneralizedEigenSolver< MatrixType_ >::ComplexScalar

Complex scalar type for MatrixType.

This is std::complex<Scalar> if Scalar is real (e.g., float or double) and just Scalar if Scalar is complex.

◆ ComplexVectorType

template<typename MatrixType_ >
 typedef Matrix Eigen::GeneralizedEigenSolver< MatrixType_ >::ComplexVectorType

Type for vector of complex scalar values eigenvalues as returned by alphas().

This is a column vector with entries of type ComplexScalar. The length of the vector is the size of MatrixType.

◆ EigenvectorsType

template<typename MatrixType_ >
 typedef Matrix Eigen::GeneralizedEigenSolver< MatrixType_ >::EigenvectorsType

Type for matrix of eigenvectors as returned by eigenvectors().

This is a square matrix with entries of type ComplexScalar. The size is the same as the size of MatrixType.

◆ Index

template<typename MatrixType_ >
 typedef Eigen::Index Eigen::GeneralizedEigenSolver< MatrixType_ >::Index
Deprecated:
since Eigen 3.3

◆ VectorType

template<typename MatrixType_ >
 typedef Matrix Eigen::GeneralizedEigenSolver< MatrixType_ >::VectorType

Type for vector of real scalar values eigenvalues as returned by betas().

This is a column vector with entries of type Scalar. The length of the vector is the size of MatrixType.

◆ GeneralizedEigenSolver() [1/3]

template<typename MatrixType_ >
 Eigen::GeneralizedEigenSolver< MatrixType_ >::GeneralizedEigenSolver ( )
inline

Default constructor.

The default constructor is useful in cases in which the user intends to perform decompositions via EigenSolver::compute(const MatrixType&, bool).

compute() for an example.

◆ GeneralizedEigenSolver() [2/3]

template<typename MatrixType_ >
 Eigen::GeneralizedEigenSolver< MatrixType_ >::GeneralizedEigenSolver ( Index size )
inlineexplicit

Default constructor with memory preallocation.

Like the default constructor but with preallocation of the internal data according to the specified problem size.

GeneralizedEigenSolver()

◆ GeneralizedEigenSolver() [3/3]

template<typename MatrixType_ >
 Eigen::GeneralizedEigenSolver< MatrixType_ >::GeneralizedEigenSolver ( const MatrixType & A, const MatrixType & B, bool computeEigenvectors = true )
inline

Constructor; computes the generalized eigendecomposition of given matrix pair.

Parameters
 [in] A Square matrix whose eigendecomposition is to be computed. [in] B Square matrix whose eigendecomposition is to be computed. [in] computeEigenvectors If true, both the eigenvectors and the eigenvalues are computed; if false, only the eigenvalues are computed.

This constructor calls compute() to compute the generalized eigenvalues and eigenvectors.

compute()

◆ alphas()

template<typename MatrixType_ >
 const ComplexVectorType& Eigen::GeneralizedEigenSolver< MatrixType_ >::alphas ( ) const
inline
Returns
A const reference to the vectors containing the alpha values

This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j).

betas(), eigenvalues()

◆ betas()

template<typename MatrixType_ >
 const VectorType& Eigen::GeneralizedEigenSolver< MatrixType_ >::betas ( ) const
inline
Returns
A const reference to the vectors containing the beta values

This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j).

alphas(), eigenvalues()

◆ compute()

template<typename MatrixType >
 GeneralizedEigenSolver< MatrixType > & Eigen::GeneralizedEigenSolver< MatrixType >::compute ( const MatrixType & A, const MatrixType & B, bool computeEigenvectors = true )

Computes generalized eigendecomposition of given matrix.

Parameters
 [in] A Square matrix whose eigendecomposition is to be computed. [in] B Square matrix whose eigendecomposition is to be computed. [in] computeEigenvectors If true, both the eigenvectors and the eigenvalues are computed; if false, only the eigenvalues are computed.
Returns
Reference to *this

This function computes the eigenvalues of the real matrix matrix. The eigenvalues() function can be used to retrieve them. If computeEigenvectors is true, then the eigenvectors are also computed and can be retrieved by calling eigenvectors().

The matrix is first reduced to real generalized Schur form using the RealQZ class. The generalized Schur decomposition is then used to compute the eigenvalues and eigenvectors.

The cost of the computation is dominated by the cost of the generalized Schur decomposition.

This method reuses of the allocated data in the GeneralizedEigenSolver object.

◆ eigenvalues()

template<typename MatrixType_ >
 EigenvalueType Eigen::GeneralizedEigenSolver< MatrixType_ >::eigenvalues ( ) const
inline

Returns an expression of the computed generalized eigenvalues.

Returns
An expression of the column vector containing the eigenvalues.

It is a shortcut for

this->alphas().cwiseQuotient(this->betas());
const VectorType & betas() const
Definition: GeneralizedEigenSolver.h:226
const ComplexVectorType & alphas() const
Definition: GeneralizedEigenSolver.h:215

Not that betas might contain zeros. It is therefore not recommended to use this function, but rather directly deal with the alphas and betas vectors.

Precondition
Either the constructor GeneralizedEigenSolver(const MatrixType&,const MatrixType&,bool) or the member function compute(const MatrixType&,const MatrixType&,bool) has been called before.

The eigenvalues are repeated according to their algebraic multiplicity, so there are as many eigenvalues as rows in the matrix. The eigenvalues are not sorted in any particular order.