java.io.Serializable
public class MyEigenvalueDecomposition
extends java.lang.Object
implements java.io.Serializable
If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is diagonal and the eigenvector matrix V is orthogonal. I.e. A = V.times(D.times(V.transpose())) and V.times(V.transpose()) equals the identity matrix.
If A is not symmetric, then the eigenvalue matrix D is block diagonal with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues, lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda]. The columns of V represent the eigenvectors in the sense that A*V = V*D, i.e. A.times(V) equals V.times(D). The matrix V may be badly conditioned, or even singular, so the validity of the equation A = V*D*inverse(V) depends upon V.cond().
Constructor | Description |
---|---|
MyEigenvalueDecomposition(int n,
double[][] input) |
Check for symmetry, then construct the eigenvalue decomposition
|
Modifier and Type | Method | Description |
---|---|---|
double[][] |
getD() |
Return the block diagonal eigenvalue matrix
|
double[] |
getImagEigenvalues() |
Return the imaginary parts of the eigenvalues
|
double[] |
getRealEigenvalues() |
Return the real parts of the eigenvalues
|
double[][] |
getV() |
Return the eigenvector matrix
|
protected void |
setArray(double[][] input) |
public MyEigenvalueDecomposition(int n, double[][] input)
A
- Square matrixprotected void setArray(double[][] input)
public double[][] getV()
public double[] getRealEigenvalues()
public double[] getImagEigenvalues()
public double[][] getD()