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More generally, we can factor a complex m×n matrix A, with m ≥ n, as the product of an m×m unitary matrix Q and an m×n upper triangular matrix R.As the bottom (m−n) rows of an m×n upper triangular matrix consist entirely of zeroes, it is often useful to partition R, or both R and Q:
An RRQR factorization or rank-revealing QR factorization is a matrix decomposition algorithm based on the QR factorization which can be used to determine the rank of a matrix. [1] The singular value decomposition can be used to generate an RRQR, but it is not an efficient method to do so. [2] An RRQR implementation is available in MATLAB. [3]
Instead, the QR algorithm works with a complete basis of vectors, using QR decomposition to renormalize (and orthogonalize). For a symmetric matrix A , upon convergence, AQ = QΛ , where Λ is the diagonal matrix of eigenvalues to which A converged, and where Q is a composite of all the orthogonal similarity transforms required to get there.
The QR factorization of a matrix is a matrix and a matrix so that A = QR, where Q is orthogonal and R is upper triangular. [1]: 50 [4]: 223 The two main algorithms for computing QR factorizations are the Gram–Schmidt process and the Householder transformation.
In the theory of Lie group decompositions, it is generalized by the Iwasawa decomposition. The application of the Gram–Schmidt process to the column vectors of a full column rank matrix yields the QR decomposition (it is decomposed into an orthogonal and a triangular matrix ).
two iterations of the Givens rotation (note that the Givens rotation algorithm used here differs slightly from above) yield an upper triangular matrix in order to compute the QR decomposition. In order to form the desired matrix, zeroing elements (2, 1) and (3, 2) is required; element (2, 1) is zeroed first, using a rotation matrix of:
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.
QR decomposition should have a link to here, but they are different. QR iteration (this page) uses QR decomposition to find eigenvalues. If anything, this article could be expanded into a series of articles including multishift versions and the modern forms of the algorithm that do the QR decomposition only in a very hidden implicit form.--