Block RLS using row Householder reflections
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Publication:1260781
DOI10.1016/0024-3795(93)90464-YzbMath0781.65032MaRDI QIDQ1260781
Adam W. Bojanczyk, James G. Nagy, Robert J. Plemmons
Publication date: 25 August 1993
Published in: Linear Algebra and its Applications (Search for Journal in Brave)
performancealgorithmsCholesky factorizationNumerical experimentsmatrix-matrix BLAS-3 computationsrecursive least squares problemsrow hyperbolic Householder reflections
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Related Items (8)
Square-root Householder subspace tracking ⋮ The fast recursive row-Householder subspace tracking algorithm ⋮ Updating the principal angle decomposition ⋮ Block RLS using row Householder reflections ⋮ Inverse updating and downdating for weighted linear least squares using \(M\)-invariant reflections ⋮ Conference celebrating the 60th birthday of Robert J. Plemmons. Papers from the conference, Winston-Salem, NC, USA, January 1999 ⋮ Dedication to Robert J. Plemmons ⋮ Fast inverse Cholesky decomposition for rectangular Toeplitz-block matrix
Uses Software
Cites Work
- Analysis of a recursive least squares hyperbolic rotation algorithm for signal processing
- Least squares modifications with inverse factorizations: Parallel implications
- Adaptive Lanczos methods for recursive condition estimation
- Block RLS using row Householder reflections
- Hyperbolic householder transformations
- A Storage-Efficient $WY$ Representation for Products of Householder Transformations
- Stability Analysis of a Householder-Based Algorithm for Downdating the Cholesky Factorization
- Fast Adaptive Condition Estimation
- Modification of the Householder Method Based on the Compact WY Representation
- Reorthogonalization and Stable Algorithms for Updating the Gram-Schmidt QR Factorization
- Perturbation Bounds for the $QR$ Factorization of a Matrix
- The Effects of Rounding Error on an Algorithm for Downdating a Cholesky Factorization
- Accurate Downdating of Least Squares Solutions
- Cholesky Factor Updating Techniques for Rank 2 Matrix Modifications
- Methods for Modifying Matrix Factorizations
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