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SHIFTED CHOLESKY QR FOR COMPUTING THE QR FACTORIZATION OF ILL-CONDITIONED MATRICES
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| Title: | SHIFTED CHOLESKY QR FOR COMPUTING THE QR FACTORIZATION OF ILL-CONDITIONED MATRICES |
| Authors: | Fukaya, Takeshi Browse this author →KAKEN DB | | Kannan, Ramaseshan Browse this author | | Nakatsukasa, Yuji Browse this author | | Yamamoto, Yusaku Browse this author | | Yanagisawa, Yuka Browse this author |
| Keywords: | QR factorization | | Cholesky QR factorization | | oblique inner product | | roundoff error analysis | | communication-avoiding algorithms |
| Issue Date: | 20-Feb-2020 |
| Publisher: | Society for Industrial and Applied Mathematics(SIAM) |
| Journal Title: | SIAM Journal on Scientific Computing |
| Volume: | 42 |
| Issue: | 1 |
| Start Page: | A477 |
| End Page: | A503 |
| Publisher DOI: | 10.1137/18M1218212 |
| Abstract: | The Cholesky QR algorithm is an efficient communication-minimizing algorithm for computing the QR factorization of a tall-skinny matrix X epsilon R-mxn, where m >> n. Unfortunately it is inherently unstable and often breaks down when the matrix is ill-conditioned. A recent work [Yamamoto et al., ETNA, 44, pp. 306--326 (2015)] establishes that the instability can be cured by repeating the algorithm twice (called CholeskyQR2). However, the applicability of CholeskyQR2 is still limited by the requirement that the Cholesky factorization of the Gram matrix X-inverted perpendicular X runs to completion, which means that it does not always work for matrices X with the 2-norm condition number kappa(2)(X) roughly greater than u(-1/2), where u is the unit roundoff. In this work we extend the applicability to kappa(2)(X) = O (u(-1)) by introducing a shift to the computed Gram matrix so as to guarantee the Cholesky factorization R-inverted perpendicular R = A(inverted perpendicular) A+sI succeeds numerically. We show that the computed AR(-1) has reduced condition number that is roughly bounded by u(-1/2), for which CholeskyQR2 safely computes the QR factorization, yielding a computed Q of orthogonality vertical bar vertical bar Q(inverted perpendicular) - Q I vertical bar vertical bar(2) and residual vertical bar vertical bar A - QR vertical bar vertical bar(F) / vertical bar vertical bar A vertical bar vertical bar(F) both of the order of u. Thus we obtain the required QR factorization by essentially running Cholesky QR thrice. We extensively analyze the resulting algorithm shiftedCholeskyQR3 to reveal its excellent numerical stability. The shiftedCholeskyQR3 algorithm is also highly parallelizable, and applicable and effective also when working with an oblique inner product. We illustrate our findings through experiments, in which we achieve significant speedup over alternative methods. |
| Rights: | https://creativecommons.org/licenses/by/4.0/ |
| Type: | article |
| URI: | http://hdl.handle.net/2115/79165 |
| Appears in Collections: | 情報基盤センター (Information Initiative Center) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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