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Re: eigenvectors and spectral decomposition

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#1979
osigrtoelt
Member

Thanks for the answer, Haksun. However, I still don’t see how this should be done for a more general matrix with eigenvalues of multiplicity > 1.

Can we please implement the eigenvalue decomposition as a class like Cholesky or SVD? For a symmetric matrix A, we should have A = QDQ^{-1} = QDQ^T, where D is a diagonal matrix that contains all eigenvalues of A (counting multiplicity) and Q is an orthonormal matrix.

http://en.wikipedia.org/wiki/Eigenvalue_decomposition