# Singular Values in 2D

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Some square matrices are diagonalizable or even orthogonally diagonalizable. An important fact about diagonalization is that the resulting diagonal matrix contains the eigenvalues of the original matrix on the main diagonal. However, not all matrices are diagonalizable. In such a case, the singular value decomposition (SVD) still exists. If is an matrix, its singular values are the square roots of the eigenvalues of the matrix .

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Contributed by: Crista Arangala (March 2014)

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