FloatComplexEigDecomp Class

Class FloatComplexEigDecomp computes the eigenvalues and left and right eigenvectors of a general matrix, with preliminary balancing.

Inheritance Hierarchy

SystemObject
CenterSpace.NMath.CoreFloatComplexEigDecomp

Namespace: CenterSpace.NMath.Core
Assembly: NMath (in NMath.dll) Version: 7.4

Syntax

C++

Copy

[SerializableAttribute]
public class FloatComplexEigDecomp : ICloneable

<SerializableAttribute>
Public Class FloatComplexEigDecomp
	Implements ICloneable

[SerializableAttribute]
public ref class FloatComplexEigDecomp : ICloneable

[<SerializableAttribute>]
type FloatComplexEigDecomp = 
    class
        interface ICloneable
    end

The FloatComplexEigDecomp type exposes the following members.

Constructors

	Name	Description
	FloatComplexEigDecomp	Default constructor. Constructs an empty eigenvalue decomposition.
	FloatComplexEigDecomp(FloatComplexMatrix)	Construct a FloatComplexEigDecomp instance for the given matrix.

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Properties

	Name	Description
	Cols	Gets the number of columns in the decomposed matrix.
	EigenValues	Gets the computed eigenvalues.
	IsGood	Returns true if the all the eigenvalues and eigenvectors were successfully computed; otherwise, false.
	LeftEigenVectors	Gets the matrix of left eigenvectors.
	NumberOfEigenValues	Gets the number of eigenvalues computed.
	NumberOfLeftEigenVectors	Gets the number of left eigenvectors.
	NumberOfRightEigenVectors	Gets the number of right eigenvectors.
	RightEigenVectors	Gets the matrix of right eigenvectors.
	Rows	Gets the number of rows in the decomposed matrix.

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Methods

	Name	Description
	Clone	Creates a deep copy of this eigenvalue decompostiion.
	EigenValue	Returns the ith eigenvalue.
	Factor	Computes all the eigenvalues and eigenvectors for the given square matrix.
	FactorNoPreconditioning	Computes all the eigenvalues and eigenvectors for the given square matrix.
	LeftEigenVector	Returns the ith left eigenvector.
	RightEigenVector	Returns the ith right eigenvector.

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Remarks

Balancing a matrix means permuting the rows and columns to make it more nearly upper triangular, and applying a diagonal similarity transformation to make its rows and columns closer in norm and the condition numbers of its eigenvalues and eigenvectors smaller.

Reference

CenterSpace.NMath.Core Namespace