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Eigenvectors are not orthogonal for some skew-symmetric matrices, why?

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0 -0.5000 0 0 0 0.5000
0.5000 0 -0.5000 0 0 0
0 0.5000 0 -0.5000 0 0
A = 0 0 0.5000 0 -0.5000 0
0 0 0 0.5000 0 -0.5000
-0.5000 0 0 0 0.5000 0
The above matrix is skew-symmetric. When I use [U E] = eig(A), to find the eigenvectors of the matrix. These eigenvectors must be orthogonal, i.e., U*U' matix must be Identity matrix. However, I am getting U*U' as
0.9855 -0.0000 0.0410 -0.0000 -0.0265 0.0000
-0.0000 0.9590 0.0000 0.0265 -0.0000 0.0145
0.0410 0.0000 0.9735 -0.0000 -0.0145 0.0000
-0.0000 0.0265 -0.0000 1.0145 0.0000 -0.0410
-0.0265 -0.0000 -0.0145 0.0000 1.0410 -0.0000
0.0000 0.0145 0.0000 -0.0410 -0.0000 1.0265
Here we can observe a substantial error. This happens for some other skew-symmetric matrices also. Why this large error is being observed and how do I get correct eigen-decomposition for all skew-symmetric matrices?


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Accepted Answer

Roger Stafford
Roger Stafford on 1 May 2015
Edited: Walter Roberson on 20 Sep 2018
Your matrix A is "defective" , meaning that its eigenvalues are not all distinct. In fact, it has only three distinct eigenvalues. Consequently the space of eigenvectors does not fully span six-dimensional vector space. See the Wikipedia article:
What you are seeing is not an error on Matlab's part. It is a mathematical property of such matrices. You cannot achieve what you call "correct eigen-decomposition" for such matrices.


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Roger Stafford
Roger Stafford on 2 May 2015
@Rahul: Once the repetition of eigenvalues is recognized, the adjustment can easily be done using matlab's 'orth' function. In the case of your particular A, the first and fourth eigenvalues in diag(E) are equal (on my computer), and similarly for the second and fifth, and for the third and sixth, so the adjustment (in my case) would simply be:
U(:,[1,4]) = orth(U(:,[1,4]));
U(:,[2,5]) = orth(U(:,[2,5]));
U(:,[3,6]) = orth(U(:,[3,6]));
Note that the resulting U, besides being orthonormal, will still constitute a valid set of eigenvectors, even though there is an arbitrary aspect to the result of 'orth'.
The hard part is recognizing such eigenvalue equalities and in fact recognizing matrices that are diagonalizable. There really are matrices that cannot be diagonalized and which are therefore designated as "defective". For these reasons it ought to be Mathworks that carries out such a revision.
However, you can experiment on your own using 'orth' to see how it works. Remember, both the eigenvalues and the eigenvectors will be complex-valued for your skew-symmetric matrices, and in testing the adjusted U'*U you will get tiny imaginary components due to rounding errors.
By the way, in requesting a change, you should probably not refer to the current version as being in "error", since Mathworks up to this point hasn't promised orthonormal eigenvectors for matrices that are other than real and symmetric.
Lorenzo on 20 Sep 2018
That matrix is not defective (1i times the matrix is hermitian and so it has a complete set of eigenvectors), it has however degenerate eigenvalues and this is the reason why U fails to be unitary. You should use schur in this case, which always return a unitary matrix

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More Answers (2)

Rahul Singh
Rahul Singh on 2 May 2015
Is there any other way (other than Matlab) of computing orthogonal eigenvectors for this particular skew-symmetric matrix ? I tried NumPy package also, which gave me same results as Matlab.


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Roger Stafford
Roger Stafford on 2 May 2015
The adjusted U'*U and U*U' values using 'orth' are not significantly different on my computer. They differ only from the 6 x 6 identity matrix and from each other out at the 15-th or 16-th decimal place which is what can be expected from round-off errors. Correspondingly tiny imaginary parts are also present, again due to round-off errors. Are you sure you have like eigenvalues matched? Yours may have been given in a different order from mine.
Roger Stafford
Roger Stafford on 3 May 2015
Yes, all the eigenvectors come out orthogonal after that adjustment I described. The fact that U'*U gives the identity matrix implies that. You should be able to check that for yourself.

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Christine Tobler
Christine Tobler on 20 Sep 2018
Since, as Lorenzo points out in a comment above, 1i*A is hermitian, you could apply eig to that matrix:
>> [U, D] = eig(1i*A);
>> D = D/1i;
>> norm(U'*U - eye(6))
ans =
>> norm(A*U - U*D)
ans =


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