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  2. Triangular matrix - Wikipedia

    en.wikipedia.org/wiki/Triangular_matrix

    The inverse of an upper triangular matrix, if it exists, is upper triangular. The product of an upper triangular matrix and a scalar is upper triangular. Together these facts mean that the upper triangular matrices form a subalgebra of the associative algebra of square matrices for a given size.

  3. Jordan normal form - Wikipedia

    en.wikipedia.org/wiki/Jordan_normal_form

    The Jordan normal form is the most convenient for computation of the matrix functions (though it may be not the best choice for computer computations). Let f(z) be an analytical function of a complex argument. Applying the function on a n×n Jordan block J with eigenvalue λ results in an upper triangular matrix:

  4. Gaussian elimination - Wikipedia

    en.wikipedia.org/wiki/Gaussian_elimination

    Using row operations to convert a matrix into reduced row echelon form is sometimes called Gauss–Jordan elimination. In this case, the term Gaussian elimination refers to the process until it has reached its upper triangular, or (unreduced) row echelon form. For computational reasons, when solving systems of linear equations, it is sometimes ...

  5. Bruhat decomposition - Wikipedia

    en.wikipedia.org/wiki/Bruhat_decomposition

    In mathematics, the Bruhat decomposition (introduced by François Bruhat for classical groups and by Claude Chevalley in general) = of certain algebraic groups = into cells can be regarded as a general expression of the principle of Gauss–Jordan elimination, which generically writes a matrix as a product of an upper triangular and lower triangular matrices—but with exceptional cases.

  6. Matrix decomposition - Wikipedia

    en.wikipedia.org/wiki/Matrix_decomposition

    One can always write = where V is a real orthogonal matrix, is the transpose of V, and S is a block upper triangular matrix called the real Schur form. The blocks on the diagonal of S are of size 1×1 (in which case they represent real eigenvalues) or 2×2 (in which case they are derived from complex conjugate eigenvalue pairs).

  7. 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.

  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. Heisenberg group - Wikipedia

    en.wikipedia.org/wiki/Heisenberg_group

    If a, b, c, are real numbers (in the ring R), then one has the continuous Heisenberg group H 3 (R).. It is a nilpotent real Lie group of dimension 3.. In addition to the representation as real 3×3 matrices, the continuous Heisenberg group also has several different representations in terms of function spaces.