enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. 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 ...

  3. 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 ...

  4. Broyden's method - Wikipedia

    en.wikipedia.org/wiki/Broyden's_method

    The above equation is underdetermined when k is greater than one. Broyden suggested using the most recent estimate of the Jacobian matrix, J n−1, and then improving upon it by requiring that the new form is a solution to the most recent secant equation, and that there is minimal modification to J n−1:

  5. Rank factorization - Wikipedia

    en.wikipedia.org/wiki/Rank_factorization

    Every finite-dimensional matrix has a rank decomposition: Let be an matrix whose column rank is . Therefore, there are r {\textstyle r} linearly independent columns in A {\textstyle A} ; equivalently, the dimension of the column space of A {\textstyle A} is r {\textstyle r} .

  6. Row and column spaces - Wikipedia

    en.wikipedia.org/wiki/Row_and_column_spaces

    The dimension of the column space is called the rank of the matrix. The rank is equal to the number of pivots in the reduced row echelon form, and is the maximum number of linearly independent columns that can be chosen from the matrix. For example, the 4 × 4 matrix in the example above has rank three.

  7. QR decomposition - Wikipedia

    en.wikipedia.org/wiki/QR_decomposition

    To solve the underdetermined (<) linear problem = where the matrix has dimensions and rank , first find the QR factorization of the transpose of : =, where Q is an orthogonal matrix (i.e. =), and R has a special form: = [].

  8. Low-rank approximation - Wikipedia

    en.wikipedia.org/wiki/Low-rank_approximation

    In mathematics, low-rank approximation refers to the process of approximating a given matrix by a matrix of lower rank. More precisely, it is a minimization problem, in which the cost function measures the fit between a given matrix (the data) and an approximating matrix (the optimization variable), subject to a constraint that the approximating matrix has reduced rank.

  9. Singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Singular_value_decomposition

    ⁠ The second step is to compute the SVD of the bidiagonal matrix. This step can only be done with an iterative method (as with eigenvalue algorithms). However, in practice it suffices to compute the SVD up to a certain precision, like the machine epsilon.