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Every real symmetric matrix is Hermitian, and therefore all its eigenvalues are real. (In fact, the eigenvalues are the entries in the diagonal matrix D {\displaystyle D} (above), and therefore D {\displaystyle D} is uniquely determined by A {\displaystyle A} up to the order of its entries.)
If a matrix A can be eigendecomposed and if none of its eigenvalues are zero, then A is invertible and its inverse is given by = If is a symmetric matrix, since is formed from the eigenvectors of , is guaranteed to be an orthogonal matrix, therefore =.
In case of a symmetric matrix it is the absolute value of the quotient of the largest and smallest eigenvalue. Matrices with large condition numbers can cause numerically unstable results: small perturbation can result in large errors. Hilbert matrices are the most famous ill-conditioned matrices.
The eigenvalues of a Hermitian matrix are real, since (λ − λ)v = (A * − A)v = (A − A)v = 0 for a non-zero eigenvector v. If A is real, there is an orthonormal basis for R n consisting of eigenvectors of A if and only if A is symmetric. It is possible for a real or complex matrix to have all real eigenvalues without being Hermitian.
If the linear transformation is expressed in the form of an n by n matrix A, then the eigenvalue equation for a linear transformation above can be rewritten as the matrix multiplication =, where the eigenvector v is an n by 1 matrix. For a matrix, eigenvalues and eigenvectors can be used to decompose the matrix—for example by diagonalizing it.
Also, recall that a Hermitian (or real symmetric) matrix has real eigenvalues. It can be shown [9] that, for a given matrix, the Rayleigh quotient reaches its minimum value (the smallest eigenvalue of M) when is (the corresponding eigenvector).
We will now discuss how these difficulties manifest in the basic QR algorithm. This is illustrated in Figure 2. Recall that the ellipses represent positive-definite symmetric matrices. As the two eigenvalues of the input matrix approach each other, the input ellipse changes into a circle. A circle corresponds to a multiple of the identity matrix.
Real skew-symmetric matrices are normal matrices (they commute with their adjoints) and are thus subject to the spectral theorem, which states that any real skew-symmetric matrix can be diagonalized by a unitary matrix. Since the eigenvalues of a real skew-symmetric matrix are imaginary, it is not possible to diagonalize one by a real matrix.