Difference in calculating eigenvectors wih Java and Python

Question

As a current task, I need to calculate eigenvalues and eigenvectors for a 120*120 matrix. For start, I tested those calculations on a simple 2 by 2 matrix in both Java (Apache Commons Math library) and Python 2.7 (Numpy library). I have a problem with eigenvector values not matching, as show below :

//Java
import org.apache.commons.math3.linear.EigenDecomposition;
import org.apache.commons.math3.linear.MatrixUtils;
import org.apache.commons.math3.linear.RealMatrix;

public class TemporaryTest {

  public static void main(String[] args) {

    double[][] testArray = {{2, -1}, {1, 1}};
    RealMatrix testMatrix = MatrixUtils.createRealMatrix(testArray);
    EigenDecomposition decomposition = new EigenDecomposition (testMatrix);
    System.out.println("eigenvector[0] = " + decomposition.getEigenvector(0));
    System.out.println("eigenvector[1] = " + decomposition.getEigenvector(1));
}

Output of eigenvectors are shown as {real_value + imaginary_value; real_value + imaginary_value}:

//Java output
eigenvector[0] = {-0.8660254038; 0}
eigenvector[1] = {0.5; 1}

Same code in Python, but using Numpy library:

# Python
import numpy as np
from numpy import linalg as LA

w, v = LA.eig(np.array([[2, -1], [1, 1]]))
print (v[:, 0])
print (v[:, 1])

Output of eigenvectors in Python are shown similarly, [real+imag real+imag]:

[ 0.35355339+0.61237244j  0.70710678+0.j        ]
[ 0.35355339-0.61237244j  0.70710678-0.j        ]

My concern is, why are those vectors different ? Is there something that I am missing ? Ty for any kind of help or advice

Those vectors in the Java output are not eigenvectors. getEigenvector(i) returns a RealVector, but your matrix has complex eigenvalues and eigenvectors. I don't know how the Apache Commons Math Library represents a complex eigenvector; hopefully someone familiar with the library will help you translate the real values returned by the Java functions into the actual complex eigenvectors. — Warren Weckesser
I agree, but as you can see, even the real values do not match :-/ — borgmater
By any chance is your 120*120 matrix symmetric? If so, you won't have to deal with the problem of the EigenDecomposition methods returning only real values. — Warren Weckesser

Warren Weckesser Warren Weckesser · Accepted Answer · 2016-11-17T20:47:08

In Apache Commons Math 3, EigenDecomposition accepts nonsymmetric matrices, but it returns results using the classes RealVector and RealMatrix. To get the actual complex results, you have to combine the appropriate real results into complex conjugate pairs.

In the case of the eigenvectors, you got:

eigenvector[0] = {-0.8660254038; 0}
eigenvector[1] = {0.5; 1}

Both those vectors are associated with the complex conjugate pair of eigenvalues getRealEigenvalue(0) + getImagEigenvalue(0)*i and getRealEigenvalue(1) + getImagEigenvalue(1)*i, but those vectors are not the actual eigenvectors. The actual eigenvectors are the complex conjugate pairs eigenvector[0] + eigenvector[1]*i and eigenvector[0] - eigenvector[1]*i.

Those vectors still don't "match" the results returned by numpy, but that is because the two libraries have not used the same normalization. Eigenvectors are not unique; an eigenvector multiplied by any nonzero scalar (including a complex scalar) is still an eigenvector. The only difference between the Java result and the numpy result is a scalar multiplier.

For convenience, I'll convert the floating point values to their exact values. That is, -0.8660254038 is the floating point approximation of -sqrt(3)/2. The Java math library is giving the following eigenvectors:

 [-sqrt(3)/2 + (1/2)*i]    and    [-sqrt(3)/2 - (1/2)*i]
 [      0    +     1*i]           [      0    -     1*i]

If you multiply the first eigenvector by -(sqrt(2)/2)*i and the second by (sqrt(2)/2)*i, you'll get the eigenvectors that were return by numpy.

Here's an ipython session with that calculation. v1 and v2 are the vectors shown above.

In [20]: v1 = np.array([-np.sqrt(3)/2 + 0.5j, 1j])

In [21]: v1
Out[21]: array([-0.8660254+0.5j,  0.0000000+1.j ])

In [22]: v2 = np.array([-np.sqrt(3)/2 - 0.5j, -1j])

In [23]: v2
Out[23]: array([-0.8660254-0.5j,  0.0000000-1.j ])

Multiply v1 by -(sqrt(2)/2)*i to get the first eigenvector returned by numpy.linalg.eig:

In [24]: v1*(-np.sqrt(2)/2*1j)
Out[24]: array([ 0.35355339+0.61237244j,  0.70710678-0.j        ])

Multiply v2 by (sqrt(2)/2)*i to get the second eigenvector returned by numpy.linalg.eig:

In [25]: v2*(np.sqrt(2)/2*1j)
Out[25]: array([ 0.35355339-0.61237244j,  0.70710678+0.j        ])

For convenience, here's a repeat of the numpy calculation. The columns of evecs are the eigenvectors.

In [28]: evals, evecs = np.linalg.eig(a)

In [29]: evecs
Out[29]: 
array([[ 0.35355339+0.61237244j,  0.35355339-0.61237244j],
       [ 0.70710678+0.j        ,  0.70710678-0.j        ]])

Difference in calculating eigenvectors wih Java and Python

2 Answers