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  2. Matrix norm - Wikipedia

    en.wikipedia.org/wiki/Matrix_norm

    Suppose a vector norm ‖ ‖ on and a vector norm ‖ ‖ on are given. Any matrix A induces a linear operator from to with respect to the standard basis, and one defines the corresponding induced norm or operator norm or subordinate norm on the space of all matrices as follows: ‖ ‖, = {‖ ‖: ‖ ‖ =} = {‖ ‖ ‖ ‖:} . where denotes the supremum.

  3. Smith normal form - Wikipedia

    en.wikipedia.org/wiki/Smith_normal_form

    In mathematics, the Smith normal form (sometimes abbreviated SNF [1]) is a normal form that can be defined for any matrix (not necessarily square) with entries in a principal ideal domain (PID). The Smith normal form of a matrix is diagonal, and can be obtained from the original matrix by multiplying on the left and right by invertible square ...

  4. Normal matrix - Wikipedia

    en.wikipedia.org/wiki/Normal_matrix

    Thus = and = where is a diagonal matrix whose diagonal values are in general complex. The left and right singular vectors in the singular value decomposition of a normal matrix A = U D V ∗ {\displaystyle A=UDV^{*}} differ only in complex phase from each other and from the corresponding eigenvectors, since the phase must be factored out of the ...

  5. Jacobi eigenvalue algorithm - Wikipedia

    en.wikipedia.org/wiki/Jacobi_eigenvalue_algorithm

    The 2-norm of a matrix A is the norm based on the Euclidean vectornorm; that is, the largest value ‖ ‖ when x runs through all vectors with ‖ ‖ =. It is the largest singular value of . In case of a symmetric matrix it is the largest absolute value of its eigenvectors and thus equal to its spectral radius.

  6. Diagonal matrix - Wikipedia

    en.wikipedia.org/wiki/Diagonal_matrix

    The adjugate of a diagonal matrix is again diagonal. Where all matrices are square, A matrix is diagonal if and only if it is triangular and normal. A matrix is diagonal if and only if it is both upper-and lower-triangular. A diagonal matrix is symmetric. The identity matrix I n and zero matrix are diagonal. A 1×1 matrix is always diagonal.

  7. Jordan normal form - Wikipedia

    en.wikipedia.org/wiki/Jordan_normal_form

    If A is a real matrix, its Jordan form can still be non-real. Instead of representing it with complex eigenvalues and ones on the superdiagonal, as discussed above, there exists a real invertible matrix P such that P −1 AP = J is a real block diagonal matrix with each block being a real Jordan block. [15]

  8. Schur decomposition - Wikipedia

    en.wikipedia.org/wiki/Schur_decomposition

    Write the triangular matrix U as U = D + N, where D is diagonal and N is strictly upper triangular (and thus a nilpotent matrix). The diagonal matrix D contains the eigenvalues of A in arbitrary order (hence its Frobenius norm, squared, is the sum of the squared moduli of the eigenvalues of A, while the Frobenius norm of A, squared, is the sum ...

  9. Operator norm - Wikipedia

    en.wikipedia.org/wiki/Operator_norm

    Matrix normNorm on a vector space of matrices; Norm (mathematics) – Length in a vector space; Normed space – Vector space on which a distance is defined; Operator algebra – Branch of functional analysis; Operator theory – Mathematical field of study