Browsing Japanese translation

8493 of 132 results
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
91.
Cp is a pointer to the Cholesky fact. (the memory is outside scilab space)
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Cp はコレスキー分解のポインタです. (メモリはScilab空間の外です)
Translated and reviewed by Rui Hirokawa
92.
2/ for solving a linear system A*x = b then use:
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2/ 線形システム A*x = b を特には次のようします:
Translated and reviewed by Rui Hirokawa
93.
x = taucs_chsolve(Cp,b)
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x = taucs_chsolve(Cp,b)
Translated and reviewed by Rui Hirokawa
8493 of 132 results

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