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  2. Singular value - Wikipedia

    en.wikipedia.org/wiki/Singular_value

    The smallest singular value of a matrix A is σ n (A). It has the following properties for a non-singular matrix A: The 2-norm of the inverse matrix (A −1) equals the inverse σ n −1 (A). [1]: Thm.3.3 The absolute values of all elements in the inverse matrix (A −1) are at most the inverse σ n −1 (A). [1]: Thm.3.3

  3. Eigendecomposition of a matrix - Wikipedia

    en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix

    Let A be a square n × n matrix with n linearly independent eigenvectors q i (where i = 1, ..., n).Then A can be factored as = where Q is the square n × n matrix whose i th column is the eigenvector q i of A, and Λ is the diagonal matrix whose diagonal elements are the corresponding eigenvalues, Λ ii = λ i.

  4. Eigenvalues and eigenvectors - Wikipedia

    en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors

    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.

  5. Singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Singular_value_decomposition

    The singular value decomposition is very general in the sense that it can be applied to any ⁠ ⁠ matrix, whereas eigenvalue decomposition can only be applied to square diagonalizable matrices. Nevertheless, the two decompositions are related.

  6. Matrix norm - Wikipedia

    en.wikipedia.org/wiki/Matrix_norm

    The spectral norm of a matrix is the largest singular value of , i.e., the square root of the largest eigenvalue of the matrix , where denotes the conjugate transpose of : [5] ‖ ‖ = = (). where () represents the largest singular value of matrix .

  7. Matrix pencil - Wikipedia

    en.wikipedia.org/wiki/Matrix_pencil

    Matrix pencils play an important role in numerical linear algebra.The problem of finding the eigenvalues of a pencil is called the generalized eigenvalue problem.The most popular algorithm for this task is the QZ algorithm, which is an implicit version of the QR algorithm to solve the eigenvalue problem = without inverting the matrix (which is impossible when is singular, or numerically ...

  8. Nonlinear eigenproblem - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_eigenproblem

    The NLEVP collection of nonlinear eigenvalue problems is a MATLAB package containing many nonlinear eigenvalue problems with various properties. [ 6 ] The FEAST eigenvalue solver is a software package for standard eigenvalue problems as well as nonlinear eigenvalue problems, designed from density-matrix representation in quantum mechanics ...

  9. Weyl's inequality - Wikipedia

    en.wikipedia.org/wiki/Weyl's_inequality

    Let be a matrix with .Its singular values are the positive eigenvalues of the (+) (+) Hermitian augmented matrix [].Therefore, Weyl's eigenvalue perturbation inequality for Hermitian matrices extends naturally to perturbation of singular values. [1]