Computing Small Singular Values of Bidiagonal Matrices With Guaranteed High Rela
Computing Small Singular Values of Bidiagonal Matrices With Guaranteed High Rela
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In our eReader you can find the full English version of the book. Read Computing Small Singular Values of Bidiagonal Matrices With Guaranteed High Rela Online - link to read the book on full screen. Our eReader also allows you to upload and read Pdf, Txt, ePub and fb2 books. In the Mini eReder on the page below you can quickly view all pages of the book - Read Book Computing Small Singular Values of Bidiagonal Matrices With Guaranteed High Rela
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83 1. 15 . 19 . 92 2. 19 6 . 12 . 45 1. 20 . 52 . 84 1. 14 . 22 . 87 2. 01 7 . 08 . 22 1. 11 . 28 . 50 1. 16 . 11 . 41 2. 46 8 . 12 . 21 . 37 . 53 . 66 . 81 . 21 . 32 . 49 9 . 08 . 18 . 30 . 45 . 55 . 61 . 15 . 24 . 33 10 . 06 . 16 . 25 . 42 . 47 . 55 . 12 . 21 . 29 11 . 32 . 40 . 45 . 48 . 51 . 55 . 30 . 36 . 42 12 . 27 . 27 . 27 . 52 . 52 . 52 . 29 . 29 . 29 As mentioned at the end of section 4 on convergence criteria, we may use the much less stringent criteria (4. 3) and (4. 4) if only abso...lute accuracy rather than relative accuracy in the singular values is desired. In Table 4 we show timing comparisons between LINPACK SVD and the new algorithm where each singular value is computed to an absolute accuracy of to/||A|| = 10"'*-||A|1. The format is the same as in Table 2.
As can be seen from Table 4, the absolute convergence criterion almost always leads to faster convergence than the LINPACK SVD. In fact, that it is not uniformly faster is some- what surprising. Those occasions when the new algorithm takes more rotations than LIN- PACK seem to occur because our decision algorithm for deciding whether to chase the bulge from top to bottom or bottom to top makes a bad decision (we always chase from the large end to the small end).
What to read after Computing Small Singular Values of Bidiagonal Matrices With Guaranteed High Rela?
You can find similar books in the "Read Also" column, or choose other free books by James Demmel to read onlineMoreLess
To quickly assess the difficulty of the text, read a short excerpt:
83 1. 15 . 19 . 92 2. 19 6 . 12 . 45 1. 20 . 52 . 84 1. 14 . 22 . 87 2. 01 7 . 08 . 22 1. 11 . 28 . 50 1. 16 . 11 . 41 2. 46 8 . 12 . 21 . 37 . 53 . 66 . 81 . 21 . 32 . 49 9 . 08 . 18 . 30 . 45 . 55 . 61 . 15 . 24 . 33 10 . 06 . 16 . 25 . 42 . 47 . 55 . 12 . 21 . 29 11 . 32 . 40 . 45 . 48 . 51 . 55 . 30 . 36 . 42 12 . 27 . 27 . 27 . 52 . 52 . 52 . 29 . 29 . 29 As mentioned at the end of section 4 on convergence criteria, we may use the much less stringent criteria (4. 3) and (4. 4) if only abso...lute accuracy rather than relative accuracy in the singular values is desired. In Table 4 we show timing comparisons between LINPACK SVD and the new algorithm where each singular value is computed to an absolute accuracy of to/||A|| = 10"'*-||A|1. The format is the same as in Table 2.
As can be seen from Table 4, the absolute convergence criterion almost always leads to faster convergence than the LINPACK SVD. In fact, that it is not uniformly faster is some- what surprising. Those occasions when the new algorithm takes more rotations than LIN- PACK seem to occur because our decision algorithm for deciding whether to chase the bulge from top to bottom or bottom to top makes a bad decision (we always chase from the large end to the small end).
What to read after Computing Small Singular Values of Bidiagonal Matrices With Guaranteed High Rela?
You can find similar books in the "Read Also" column, or choose other free books by James Demmel to read onlineMoreLess
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