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  2. Invertible matrix - Wikipedia

    en.wikipedia.org/wiki/Invertible_matrix

    The nullity theorem says that the nullity of A equals the nullity of the sub-block in the lower right of the inverse matrix, and that the nullity of B equals the nullity of the sub-block in the upper right of the inverse matrix. The inversion procedure that led to Equation performed matrix block operations that operated on C and D first.

  3. Cancellation property - Wikipedia

    en.wikipedia.org/wiki/Cancellation_property

    If det(A) = 0, then B might not equal C, because the matrix equation AX = B will not have a unique solution for a non-invertible matrix A. Also note that if AB = CA and A ≠ 0 and the matrix A is invertible (i.e. has det(A) ≠ 0), it is not necessarily true that B = C. Cancellation works only for AB = AC and BA = CA (provided that matrix A is ...

  4. Woodbury matrix identity - Wikipedia

    en.wikipedia.org/wiki/Woodbury_matrix_identity

    Nonsingularity of the latter requires that B −1 exist since it equals B(I + VA −1 UB) and the rank of the latter cannot exceed the rank of B. [7] Since B is invertible, the two B terms flanking the parenthetical quantity inverse in the right-hand side can be replaced with (B −1) −1, which results in the original Woodbury identity.

  5. Moore–Penrose inverse - Wikipedia

    en.wikipedia.org/wiki/Moore–Penrose_inverse

    In mathematics, and in particular linear algebra, the Moore–Penrose inverse ⁠ + ⁠ of a matrix ⁠ ⁠, often called the pseudoinverse, is the most widely known generalization of the inverse matrix. [1] It was independently described by E. H. Moore in 1920, [2] Arne Bjerhammar in 1951, [3] and Roger Penrose in 1955. [4]

  6. Gaussian elimination - Wikipedia

    en.wikipedia.org/wiki/Gaussian_elimination

    A variant of Gaussian elimination called Gauss–Jordan elimination can be used for finding the inverse of a matrix, if it exists. If A is an n × n square matrix, then one can use row reduction to compute its inverse matrix, if it exists. First, the n × n identity matrix is augmented to the right of A, forming an n × 2n block matrix [A | I]

  7. Jacobian matrix and determinant - Wikipedia

    en.wikipedia.org/wiki/Jacobian_matrix_and...

    When this matrix is square, that is, when the function takes the same number of variables as input as the number of vector components of its output, its determinant is referred to as the Jacobian determinant. Both the matrix and (if applicable) the determinant are often referred to simply as the Jacobian in literature. [4]

  8. Jacobi's formula - Wikipedia

    en.wikipedia.org/wiki/Jacobi's_formula

    Lemma 1. ′ =, where ′ is the differential of . This equation means that the differential of , evaluated at the identity matrix, is equal to the trace.The differential ′ is a linear operator that maps an n × n matrix to a real number.

  9. Constrained generalized inverse - Wikipedia

    en.wikipedia.org/.../Constrained_generalized_inverse

    is solvable. If the subspace is a proper subspace of , then the matrix of the unconstrained problem () may be singular even if the system matrix of the constrained problem is invertible (in that case, =). This means that one needs to use a generalized inverse for the solution of the constrained problem.