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Preprints, Working Papers, ... Year : 2023

Randomized Householder QR


This paper introduces a randomized Householder QR factorization (RHQR). This factorization can be used to obtain a well conditioned basis of a set of vectors and thus can be employed in a variety of applications. We discuss in particular the usage of this randomized Householder factorization in the Arnoldi process. Numerical experiments show that RHQR produces a well conditioned basis and an accurate factorization. We observe that for some cases, it can be more stable than Randomized Gram-Schmidt (RGS) in both single and double precision.
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Dates and versions

hal-04156310 , version 1 (07-07-2023)
hal-04156310 , version 2 (12-07-2023)
hal-04156310 , version 3 (28-07-2023)


  • HAL Id : hal-04156310 , version 3


Laura Grigori, Edouard Timsit. Randomized Householder QR. 2023. ⟨hal-04156310v3⟩
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