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8190 of 132 results
81.
4/ you may also compute the 1-norm condition number quickly with:
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82.
K1 = condestsp(A,lup)
K1 = condestsp(A) also works but in this case the lu factors are re-computed inside
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K1 = condestsp(A,lup)
K1 = condestsp(A) も動作しますが, この場合 LU分解が内部的に再計算されます
Translated and reviewed by Rui Hirokawa
83.
5/ if you don't need the lu factors anymore, it is recommended to free them with:
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5/ LU分解をもう必要としない場合, 次のように解放することを推奨します:
Translated and reviewed by Rui Hirokawa
84.
umf_ludel(lup)
if you have lost your pointer you may use umf_ludel() which frees all the current umf lu factors
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umf_ludel(lup)
ポインタを失った場合, カレントの umf LU分解を全て解放する umf_ludel()を使用可能です
Translated and reviewed by Rui Hirokawa
85.
norm of the residual =
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残差のノルム =
Translated and reviewed by Rui Hirokawa
86.
same but with refinement =
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改善処理付きの残差のノルム =
Translated and reviewed by Rui Hirokawa
87.
norm of the residual =
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残差のノルム =
Translated and reviewed by Rui Hirokawa
88.
Now we will see how to use the taucs snmf stuff on the matrix A2.
次に行列A2のtaucs snmf処理の使用法を示します.
Translated and reviewed by Rui Hirokawa
89.
This is useful and recommended when your matrix is symmetric positive definite (s.p.d.).
これは行列が正定対称(s.p.d.)の場合に便利であり推奨されます.
Translated and reviewed by Rui Hirokawa
90.
1/ the Cholesky factorization of a s.p.d. matrix A is obtained with:
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1/ 正定対称行列Aのコレスキー分解は次のように取得できます:
Translated and reviewed by Rui Hirokawa
8190 of 132 results

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Contributors to this translation: Hiroshi Saito, Rui Hirokawa, Scilab.team.