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  2. Rank (linear algebra) - Wikipedia

    en.wikipedia.org/wiki/Rank_(linear_algebra)

    A matrix that has rank min(m, n) is said to have full rank; otherwise, the matrix is rank deficient. Only a zero matrix has rank zero. f is injective (or "one-to-one") if and only if A has rank n (in this case, we say that A has full column rank). f is surjective (or "onto") if and only if A has rank m (in this case, we say that A has full row ...

  3. Sherman–Morrison formula - Wikipedia

    en.wikipedia.org/wiki/Sherman–Morrison_formula

    This formula also has application in theoretical physics. Namely, in quantum field theory, one uses this formula to calculate the propagator of a spin-1 field. [8] [circular reference] The inverse propagator (as it appears in the Lagrangian) has the form +.

  4. Invertible matrix - Wikipedia

    en.wikipedia.org/wiki/Invertible_matrix

    A has full rank: rank A = n. A has a trivial kernel: ker(A) = {0}. The linear transformation mapping x to Ax is bijective; that is, the equation Ax = b has exactly one solution for each b in K n. (Here, "bijective" can equivalently be replaced with "injective" or "surjective") The columns of A form a basis of K n. (In this statement, "basis ...

  5. Rank–nullity theorem - Wikipedia

    en.wikipedia.org/wiki/Rank–nullity_theorem

    The rank–nullity theorem is a theorem in linear algebra, which asserts: the number of columns of a matrix M is the sum of the rank of M and the nullity of M ; and the dimension of the domain of a linear transformation f is the sum of the rank of f (the dimension of the image of f ) and the nullity of f (the dimension of the kernel of f ).

  6. Moore–Penrose inverse - Wikipedia

    en.wikipedia.org/wiki/Moore–Penrose_inverse

    For the cases where ⁠ ⁠ has full row or column rank, and the inverse of the correlation matrix (⁠ ⁠ for ⁠ ⁠ with full row rank or ⁠ ⁠ for full column rank) is already known, the pseudoinverse for matrices related to ⁠ ⁠ can be computed by applying the Sherman–Morrison–Woodbury formula to update the inverse of the ...

  7. Eigendecomposition of a matrix - Wikipedia

    en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix

    If A is Hermitian and full-rank, the basis of eigenvectors may be chosen to be mutually orthogonal. The eigenvalues are real. The eigenvectors of A −1 are the same as the eigenvectors of A. Eigenvectors are only defined up to a multiplicative constant. That is, if Av = λv then cv is also an eigenvector for any scalar c ≠ 0.

  8. Vandermonde matrix - Wikipedia

    en.wikipedia.org/wiki/Vandermonde_matrix

    An m × n rectangular Vandermonde matrix such that m ≤ n has rank m if and only if all x i are distinct. An m × n rectangular Vandermonde matrix such that m ≥ n has rank n if and only if there are n of the x i that are distinct. A square Vandermonde matrix is invertible if and only if the x i are distinct. An explicit formula for the ...

  9. Matrix decomposition - Wikipedia

    en.wikipedia.org/wiki/Matrix_decomposition

    Applicable to: m-by-n matrix A of rank r Decomposition: A = C F {\displaystyle A=CF} where C is an m -by- r full column rank matrix and F is an r -by- n full row rank matrix Comment: The rank factorization can be used to compute the Moore–Penrose pseudoinverse of A , [ 2 ] which one can apply to obtain all solutions of the linear system A x ...