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  2. Invertible matrix - Wikipedia

    en.wikipedia.org/wiki/Invertible_matrix

    In linear algebra, an invertible matrix is a square matrix which has an inverse. In other words, if some other matrix is multiplied by the invertible matrix, the result can be multiplied by an inverse to undo the operation. An invertible matrix multiplied by its inverse yields the identity matrix. Invertible matrices are the same size as their ...

  3. 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

  4. List of named matrices - Wikipedia

    en.wikipedia.org/wiki/List_of_named_matrices

    Equivalently, a matrix with singular values that are either 0 or 1. Singular matrix: A square matrix that is not invertible. Unimodular matrix: An invertible matrix with entries in the integers (integer matrix) Necessarily the determinant is +1 or −1. Unipotent matrix: A square matrix with all eigenvalues equal to 1. Equivalently, A − I is ...

  5. Moore–Penrose inverse - Wikipedia

    en.wikipedia.org/wiki/Moore–Penrose_inverse

    The above procedure shows why taking the pseudoinverse is not a continuous operation: if the original matrix ⁠ ⁠ has a singular value 0 (a diagonal entry of the matrix ⁠ ⁠ above), then modifying ⁠ ⁠ slightly may turn this zero into a tiny positive number, thereby affecting the pseudoinverse dramatically as we now have to take the ...

  6. Matrix regularization - Wikipedia

    en.wikipedia.org/wiki/Matrix_regularization

    In many cases the regularization function acts on the input (or kernel) to ensure a bounded inverse by eliminating small singular values, but it can also be useful to have spectral norms that act on the matrix that is to be learned. There are a number of matrix norms that act on the singular values of the matrix.

  7. LU decomposition - Wikipedia

    en.wikipedia.org/wiki/LU_decomposition

    If is a singular matrix of rank , then it admits an LU factorization if the first leading principal minors are nonzero, although the converse is not true. [9] If a square, invertible matrix has an LDU (factorization with all diagonal entries of L and U equal to 1), then the factorization is unique. [8]

  8. Schur complement - Wikipedia

    en.wikipedia.org/wiki/Schur_complement

    If A is invertible, the Schur complement of the block A of the matrix M is the q × q matrix defined by /:=. In the case that A or D is singular, substituting a generalized inverse for the inverses on M/A and M/D yields the generalized Schur complement.

  9. Idempotent matrix - Wikipedia

    en.wikipedia.org/wiki/Idempotent_matrix

    The only non-singular idempotent matrix is the identity matrix; that is, ... the matrix is invertible and is therefore the identity matrix. Trace