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  2. Triangular matrix - Wikipedia

    en.wikipedia.org/wiki/Triangular_matrix

    In mathematics, a triangular matrix is a special kind of square matrix.A square matrix is called lower triangular if all the entries above the main diagonal are zero. . Similarly, a square matrix is called upper triangular if all the entries below the main diagonal

  3. QR decomposition - Wikipedia

    en.wikipedia.org/wiki/QR_decomposition

    The RQ decomposition transforms a matrix A into the product of an upper triangular matrix R (also known as right-triangular) and an orthogonal matrix Q. The only difference from QR decomposition is the order of these matrices. QR decomposition is Gram–Schmidt orthogonalization of columns of A, started from the first column.

  4. QR algorithm - Wikipedia

    en.wikipedia.org/wiki/QR_algorithm

    At the k-th step (starting with k = 0), we compute the QR decomposition A k = Q k R k where Q k is an orthogonal matrix (i.e., Q T = Q −1) and R k is an upper triangular matrix. We then form A k+1 = R k Q k.

  5. LU decomposition - Wikipedia

    en.wikipedia.org/wiki/LU_decomposition

    In numerical analysis and linear algebra, lower–upper (LU) decomposition or factorization factors a matrix as the product of a lower triangular matrix and an upper triangular matrix (see matrix multiplication and matrix decomposition). The product sometimes includes a permutation matrix as well.

  6. Cholesky decomposition - Wikipedia

    en.wikipedia.org/wiki/Cholesky_decomposition

    In linear algebra, the Cholesky decomposition or Cholesky factorization (pronounced / ʃ ə ˈ l ɛ s k i / shə-LES-kee) is a decomposition of a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose, which is useful for efficient numerical solutions, e.g., Monte Carlo simulations.

  7. Matrix decomposition - Wikipedia

    en.wikipedia.org/wiki/Matrix_decomposition

    Decomposition: = 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 = b {\displaystyle A\mathbf {x} =\mathbf {b} } .

  8. Schur decomposition - Wikipedia

    en.wikipedia.org/wiki/Schur_decomposition

    There is also a real Schur decomposition. If A is an n × n square matrix with real entries, then A can be expressed as [4] = where Q is an orthogonal matrix and H is either upper or lower quasi-triangular. A quasi-triangular matrix is a matrix that when expressed as a block matrix of 2 × 2 and 1 × 1 blocks is

  9. Riordan array - Wikipedia

    en.wikipedia.org/wiki/Riordan_array

    A Riordan array is an infinite lower triangular matrix, , constructed from two formal power series, () of order 0 and () of order 1, such that , = [] (). A Riordan array is an element of the Riordan group. [1]