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  2. QR decomposition - Wikipedia

    en.wikipedia.org/wiki/QR_decomposition

    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:

  3. QR algorithm - Wikipedia

    en.wikipedia.org/wiki/QR_algorithm

    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.

  4. RRQR factorization - Wikipedia

    en.wikipedia.org/wiki/RRQR_factorization

    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]

  5. Eigendecomposition of a matrix - Wikipedia

    en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix

    The decomposition can be derived from the fundamental property of eigenvectors: = = =. The linearly independent eigenvectors q i with nonzero eigenvalues form a basis (not necessarily orthonormal) for all possible products Ax, for x ∈ C n, which is the same as the image (or range) of the corresponding matrix transformation, and also the ...

  6. Gram–Schmidt process - Wikipedia

    en.wikipedia.org/wiki/Gram–Schmidt_process

    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 ).

  7. Divide-and-conquer eigenvalue algorithm - Wikipedia

    en.wikipedia.org/wiki/Divide-and-conquer_eigen...

    For the QR algorithm with a reasonable target precision, this is , whereas for divide-and-conquer it is . The reason for this improvement is that in divide-and-conquer, the Θ ( m 3 ) {\displaystyle \Theta (m^{3})} part of the algorithm (multiplying Q {\displaystyle Q} matrices) is separate from the iteration, whereas in QR, this must occur in ...

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