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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
[SerializableAttribute]
public class FloatComplexEigDecomp : ICloneable

The FloatComplexEigDecomp type exposes the following members.

Constructors
 NameDescription
Public methodFloatComplexEigDecomp Default constructor. Constructs an empty eigenvalue decomposition.
Public methodFloatComplexEigDecomp(FloatComplexMatrix) Construct a FloatComplexEigDecomp instance for the given matrix.
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Properties
 NameDescription
Public propertyCols Gets the number of columns in the decomposed matrix.
Public propertyEigenValues Gets the computed eigenvalues.
Public propertyIsGood Returns true if the all the eigenvalues and eigenvectors were successfully computed; otherwise, false.
Public propertyLeftEigenVectors Gets the matrix of left eigenvectors.
Public propertyNumberOfEigenValues Gets the number of eigenvalues computed.
Public propertyNumberOfLeftEigenVectors Gets the number of left eigenvectors.
Public propertyNumberOfRightEigenVectors Gets the number of right eigenvectors.
Public propertyRightEigenVectors Gets the matrix of right eigenvectors.
Public propertyRows Gets the number of rows in the decomposed matrix.
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Methods
 NameDescription
Public methodClone Creates a deep copy of this eigenvalue decompostiion.
Public methodEigenValue Returns the ith eigenvalue.
Public methodFactor Computes all the eigenvalues and eigenvectors for the given square matrix.
Public methodFactorNoPreconditioning Computes all the eigenvalues and eigenvectors for the given square matrix.
Public methodLeftEigenVector Returns the ith left eigenvector.
Public methodRightEigenVector 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.
See Also