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Hello, community!
So far faced nonsymmetric matrix. Normally diagonalization of this kind matrices goes through transposed left and nontransposed right eigenvectors. With the command L=eigenvecs(A,"L") and R=eigenvecs(A,"R") we are supposed to get orthogonal eigen space. This functions do not provide orthogonality in some cases. But if restoring the eigenvectors by each eigenvalue, it is.
Does anyone know if there is something special I'm missing about left-right eigenvectors commands?
Solved! Go to Solution.
I thinks this should do it.
First read the help on eigenvals en eigenvecs:
Further information on the 'conjugate transpose' is found in the help on the svds() function:
See for more information the attached Prime 4 file
Success!
Luc
I thinks this should do it.
First read the help on eigenvals en eigenvecs:
Further information on the 'conjugate transpose' is found in the help on the svds() function:
See for more information the attached Prime 4 file
Success!
Luc
Dear Luc
Thank you for the explanation. Indeed I was missing the point about the conjugate transpose when finding left eigenvectors.
Normally when working with the dynamic analisys I diagonalized the matrices (with the symmetric ones you do it with the right eigenvectors only) easily for the convolution integral to get the responce of the structure to the certain force function. But now I see that for nonsymmetric things it turns to be much harder, since these matrices are not always (I would say almost never) diagonalized with the left/right eigenvectors.
I appreciate if you can give an idea about in which direction to think or a short practical case.
I'm reading Wilkinson and the other literature for the dynamic analisys. But the math they provide seems to me very chalenging.
Regards,
Kirill