Detailed Description

template<typename _MatrixType>
class Eigen::Tridiagonalization< _MatrixType >

Tridiagonal decomposition of a selfadjoint matrix.

This is defined in the Eigenvalues module.

#include <Eigen/Eigenvalues>

Template Parameters

_MatrixType the type of the matrix of which we are computing the tridiagonal decomposition; this is expected to be an instantiation of the Matrix class template.

This class performs a tridiagonal decomposition of a selfadjoint matrix \( A \) such that: \( A = Q T Q^* \) where \( Q \) is unitary and \( T \) a real symmetric tridiagonal matrix.

A tridiagonal matrix is a matrix which has nonzero elements only on the main diagonal and the first diagonal below and above it. The Hessenberg decomposition of a selfadjoint matrix is in fact a tridiagonal decomposition. This class is used in SelfAdjointEigenSolver to compute the eigenvalues and eigenvectors of a selfadjoint matrix.

Call the function compute() to compute the tridiagonal decomposition of a given matrix. Alternatively, you can use the Tridiagonalization(const MatrixType&) constructor which computes the tridiagonal Schur decomposition at construction time. Once the decomposition is computed, you can use the matrixQ() and matrixT() functions to retrieve the matrices Q and T in the decomposition.

The documentation of Tridiagonalization(const MatrixType&) contains an example of the typical use of this class.

See also: class HessenbergDecomposition, class SelfAdjointEigenSolver

Public Types
typedef HouseholderSequence< MatrixType, typename internal::remove_all< typename CoeffVectorType::ConjugateReturnType >::type >	HouseholderSequenceType
	Return type of matrixQ()

typedef Eigen::Index	Index

typedef _MatrixType	MatrixType
	Synonym for the template parameter `_MatrixType`.

Public Member Functions
template<typename InputType>
Tridiagonalization &	compute (const EigenBase< InputType > &matrix)
	Computes tridiagonal decomposition of given matrix.

DiagonalReturnType	diagonal () const
	Returns the diagonal of the tridiagonal matrix T in the decomposition.

CoeffVectorType	householderCoefficients () const
	Returns the Householder coefficients.

HouseholderSequenceType	matrixQ () const
	Returns the unitary matrix Q in the decomposition.

MatrixTReturnType	matrixT () const
	Returns an expression of the tridiagonal matrix T in the decomposition.

const MatrixType &	packedMatrix () const
	Returns the internal representation of the decomposition.

SubDiagonalReturnType	subDiagonal () const
	Returns the subdiagonal of the tridiagonal matrix T in the decomposition.

template<typename InputType>
	Tridiagonalization (const EigenBase< InputType > &matrix)
	Constructor; computes tridiagonal decomposition of given matrix.

	Tridiagonalization (Index size=Size==Dynamic ? 2 :Size)
	Default constructor.

Member Typedef Documentation

◆ Index

template<typename _MatrixType>

typedef Eigen::Index Eigen::Tridiagonalization< _MatrixType >::Index

Deprecated: since Eigen 3.3

Constructor & Destructor Documentation

◆ Tridiagonalization() [1/2]

template<typename _MatrixType>

Eigen::Tridiagonalization< _MatrixType >::Tridiagonalization ( Index size = Size==Dynamic ? 2 : Size )

inlineexplicit

Default constructor.

Parameters

[in] size Positive integer, size of the matrix whose tridiagonal decomposition will be computed.

The default constructor is useful in cases in which the user intends to perform decompositions via compute(). The size parameter is only used as a hint. It is not an error to give a wrong size, but it may impair performance.

See also: compute() for an example.

◆ Tridiagonalization() [2/2]

template<typename _MatrixType>

template<typename InputType>

Eigen::Tridiagonalization< _MatrixType >::Tridiagonalization ( const EigenBase< InputType > & matrix )

inlineexplicit

Constructor; computes tridiagonal decomposition of given matrix.

Parameters

[in] matrix Selfadjoint matrix whose tridiagonal decomposition is to be computed.

This constructor calls compute() to compute the tridiagonal decomposition.

Example:

MatrixXd X = MatrixXd::Random(5,5);
MatrixXd A = X + X.transpose();
cout << "Here is a random symmetric 5x5 matrix:" << endl << A << endl << endl;
Tridiagonalization<MatrixXd> triOfA(A);
MatrixXd Q = triOfA.matrixQ();
cout << "The orthogonal matrix Q is:" << endl << Q << endl;
MatrixXd T = triOfA.matrixT();
cout << "The tridiagonal matrix T is:" << endl << T << endl << endl;
cout << "Q * T * Q^T = " << endl << Q * T * Q.transpose() << endl;

Output:

Here is a random symmetric 5x5 matrix:
 0.392 -0.257  0.345   0.41  0.223
-0.257   1.19  0.379 -0.578  -1.05
 0.345  0.379  0.216  0.859  0.472
  0.41 -0.578  0.859   1.66 -0.696
 0.223  -1.05  0.472 -0.696  -1.03

The orthogonal matrix Q is:
     1      0      0      0      0
     0 -0.405  -0.34 -0.398  -0.75
     0  0.543 -0.269  0.619 -0.499
     0  0.646  0.441 -0.573 -0.245
     0  0.352 -0.786 -0.362  0.358
The tridiagonal matrix T is:
  0.392   0.635       0       0       0
  0.635    1.73   0.941       0       0
      0   0.941 0.00453   -1.11       0
      0       0   -1.11   -1.18   0.501
      0       0       0   0.501    1.49

Q * T * Q^T = 
 0.392 -0.257  0.345   0.41  0.223
-0.257   1.19  0.379 -0.578  -1.05
 0.345  0.379  0.216  0.859  0.472
  0.41 -0.578  0.859   1.66 -0.696
 0.223  -1.05  0.472 -0.696  -1.03

Member Function Documentation

◆ compute()

template<typename _MatrixType>

template<typename InputType>

Tridiagonalization & Eigen::Tridiagonalization< _MatrixType >::compute ( const EigenBase< InputType > & matrix )

inline

Computes tridiagonal decomposition of given matrix.

Parameters

[in] matrix Selfadjoint matrix whose tridiagonal decomposition is to be computed.

Returns: Reference to *this

The tridiagonal decomposition is computed by bringing the columns of the matrix successively in the required form using Householder reflections. The cost is \( 4n^3/3 \) flops, where \( n \) denotes the size of the given matrix.

This method reuses of the allocated data in the Tridiagonalization object, if the size of the matrix does not change.

Example:

Tridiagonalization<MatrixXf> tri;
MatrixXf X = MatrixXf::Random(4,4);
MatrixXf A = X + X.transpose();
tri.compute(A);
cout << "The matrix T in the tridiagonal decomposition of A is: " << endl;
cout << tri.matrixT() << endl;
tri.compute(2*A); // re-use tri to compute eigenvalues of 2A
cout << "The matrix T in the tridiagonal decomposition of 2A is: " << endl;
cout << tri.matrixT() << endl;

Output:

The matrix T in the tridiagonal decomposition of A is: 
 0.392   1.53      0      0
  1.53  0.414   1.14      0
     0   1.14  0.151 -0.287
     0      0 -0.287    1.1
The matrix T in the tridiagonal decomposition of 2A is: 
 0.784   3.05      0      0
  3.05  0.828   2.27      0
     0   2.27  0.301 -0.573
     0      0 -0.573    2.2

◆ diagonal()

template<typename MatrixType>

Tridiagonalization< MatrixType >::DiagonalReturnType Eigen::Tridiagonalization< MatrixType >::diagonal ( ) const

Returns the diagonal of the tridiagonal matrix T in the decomposition.

Returns: expression representing the diagonal of T

Precondition: Either the constructor Tridiagonalization(const MatrixType&) or the member function compute(const MatrixType&) has been called before to compute the tridiagonal decomposition of a matrix.

Example:

MatrixXcd X = MatrixXcd::Random(4,4);
MatrixXcd A = X + X.adjoint();
cout << "Here is a random self-adjoint 4x4 matrix:" << endl << A << endl << endl;
 
Tridiagonalization<MatrixXcd> triOfA(A);
MatrixXd T = triOfA.matrixT();
cout << "The tridiagonal matrix T is:" << endl << T << endl << endl;
 
cout << "We can also extract the diagonals of T directly ..." << endl;
VectorXd diag = triOfA.diagonal();
cout << "The diagonal is:" << endl << diag << endl; 
VectorXd subdiag = triOfA.subDiagonal();
cout << "The subdiagonal is:" << endl << subdiag << endl;

Output:

Here is a random self-adjoint 4x4 matrix:
      (-1.65,0)  (0.351,-0.413)    (1.47,0.238)  (0.0978,-1.11)
  (0.351,0.413)      (-0.889,0)   (0.226,0.724)  (-0.957,0.351)
  (1.47,-0.238)  (0.226,-0.724)       (-1.43,0)  (-1.71,-0.412)
  (0.0978,1.11) (-0.957,-0.351)   (-1.71,0.412)           (2,0)

The tridiagonal matrix T is:
-1.65 -1.94     0     0
-1.94 0.218 -2.21     0
    0 -2.21 0.586  1.55
    0     0  1.55 -1.13

We can also extract the diagonals of T directly ...
The diagonal is:
-1.65
0.218
0.586
-1.13
The subdiagonal is:
-1.94
-2.21
 1.55

See also: matrixT(), subDiagonal()

◆ householderCoefficients()

template<typename _MatrixType>

CoeffVectorType Eigen::Tridiagonalization< _MatrixType >::householderCoefficients ( ) const

inline

Returns the Householder coefficients.

Returns: a const reference to the vector of Householder coefficients

Precondition: Either the constructor Tridiagonalization(const MatrixType&) or the member function compute(const MatrixType&) has been called before to compute the tridiagonal decomposition of a matrix.

The Householder coefficients allow the reconstruction of the matrix \( Q \) in the tridiagonal decomposition from the packed data.

Example:

Matrix4d X = Matrix4d::Random(4,4);
Matrix4d A = X + X.transpose();
cout << "Here is a random symmetric 4x4 matrix:" << endl << A << endl;
Tridiagonalization<Matrix4d> triOfA(A);
Vector3d hc = triOfA.householderCoefficients();
cout << "The vector of Householder coefficients is:" << endl << hc << endl;

Output:

Here is a random symmetric 4x4 matrix:
 0.392  -1.03 -0.797  0.788
 -1.03   1.13  0.267  0.778
-0.797  0.267   1.07 -0.187
 0.788  0.778 -0.187 -0.541
The vector of Householder coefficients is:
1.68
1.49
   0

See also: packedMatrix(), Householder module

◆ matrixQ()

template<typename _MatrixType>

HouseholderSequenceType Eigen::Tridiagonalization< _MatrixType >::matrixQ ( ) const

inline

Returns the unitary matrix Q in the decomposition.

Returns: object representing the matrix Q

Precondition: Either the constructor Tridiagonalization(const MatrixType&) or the member function compute(const MatrixType&) has been called before to compute the tridiagonal decomposition of a matrix.

This function returns a light-weight object of template class HouseholderSequence. You can either apply it directly to a matrix or you can convert it to a matrix of type MatrixType.

See also: Tridiagonalization(const MatrixType&) for an example, matrixT(), class HouseholderSequence

◆ matrixT()

template<typename _MatrixType>

MatrixTReturnType Eigen::Tridiagonalization< _MatrixType >::matrixT ( ) const

inline

Returns an expression of the tridiagonal matrix T in the decomposition.

Returns: expression object representing the matrix T

Precondition: Either the constructor Tridiagonalization(const MatrixType&) or the member function compute(const MatrixType&) has been called before to compute the tridiagonal decomposition of a matrix.

Currently, this function can be used to extract the matrix T from internal data and copy it to a dense matrix object. In most cases, it may be sufficient to directly use the packed matrix or the vector expressions returned by diagonal() and subDiagonal() instead of creating a new dense copy matrix with this function.

See also: Tridiagonalization(const MatrixType&) for an example, matrixQ(), packedMatrix(), diagonal(), subDiagonal()

◆ packedMatrix()

template<typename _MatrixType>

const MatrixType & Eigen::Tridiagonalization< _MatrixType >::packedMatrix ( ) const

inline

Returns the internal representation of the decomposition.

Returns: a const reference to a matrix with the internal representation of the decomposition.

Precondition: Either the constructor Tridiagonalization(const MatrixType&) or the member function compute(const MatrixType&) has been called before to compute the tridiagonal decomposition of a matrix.

The returned matrix contains the following information:

the strict upper triangular part is equal to the input matrix A.
the diagonal and lower sub-diagonal represent the real tridiagonal symmetric matrix T.
the rest of the lower part contains the Householder vectors that, combined with Householder coefficients returned by householderCoefficients(), allows to reconstruct the matrix Q as \( Q = H_{N-1} \ldots H_1 H_0 \). Here, the matrices \( H_i \) are the Householder transformations \( H_i = (I - h_i v_i v_i^T) \) where \( h_i \) is the \( i \)th Householder coefficient and \( v_i \) is the Householder vector defined by \( v_i = [ 0, \ldots, 0, 1, M(i+2,i), \ldots, M(N-1,i) ]^T \) with M the matrix returned by this function.

See LAPACK for further details on this packed storage.

Example:

Matrix4d X = Matrix4d::Random(4,4);
Matrix4d A = X + X.transpose();
cout << "Here is a random symmetric 4x4 matrix:" << endl << A << endl;
Tridiagonalization<Matrix4d> triOfA(A);
Matrix4d pm = triOfA.packedMatrix();
cout << "The packed matrix M is:" << endl << pm << endl;
cout << "The diagonal and subdiagonal corresponds to the matrix T, which is:" 
     << endl << triOfA.matrixT() << endl;

Output:

Here is a random symmetric 4x4 matrix:
 0.392  -1.03 -0.797  0.788
 -1.03   1.13  0.267  0.778
-0.797  0.267   1.07 -0.187
 0.788  0.778 -0.187 -0.541
The packed matrix M is:
 0.392  -1.03 -0.797  0.788
  1.53  0.414  0.267  0.778
 0.311   1.14  0.151 -0.187
-0.308  0.588 -0.287    1.1
The diagonal and subdiagonal corresponds to the matrix T, which is:
 0.392   1.53      0      0
  1.53  0.414   1.14      0
     0   1.14  0.151 -0.287
     0      0 -0.287    1.1

See also: householderCoefficients()

◆ subDiagonal()

template<typename MatrixType>

Tridiagonalization< MatrixType >::SubDiagonalReturnType Eigen::Tridiagonalization< MatrixType >::subDiagonal ( ) const

Returns the subdiagonal of the tridiagonal matrix T in the decomposition.

Returns: expression representing the subdiagonal of T

Precondition: Either the constructor Tridiagonalization(const MatrixType&) or the member function compute(const MatrixType&) has been called before to compute the tridiagonal decomposition of a matrix.

See also: diagonal() for an example, matrixT()

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

Tridiagonalization.h

Detailed Description

Public Types

Public Member Functions

Member Typedef Documentation

◆ Index

Constructor & Destructor Documentation

◆ Tridiagonalization() [1/2]

◆ Tridiagonalization() [2/2]

Member Function Documentation

◆ compute()

◆ diagonal()

◆ householderCoefficients()

◆ matrixQ()

◆ matrixT()

◆ packedMatrix()

◆ subDiagonal()