Browsing Japanese translation

8897 of 132 results
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
94.
3/ for the same thing with one refinement step, use:
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3/ 改善ステップを付けて同様のことを行うには, 以下のようにします:
Translated and reviewed by Rui Hirokawa
95.
xr = taucs_chsolve(Cp,b,A)
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xr = taucs_chsolve(Cp,b,A)
Translated and reviewed by Rui Hirokawa
96.
4/ you may also compute the 2-norm condition number with:
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4/ 次のように2次ノルム条件数を計算することも可能です:
Translated and reviewed by Rui Hirokawa
97.
[K2, lm, vm, lM, vM] = cond2sp(A, Cp [, itermax, eps, verb])
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[K2, lm, vm, lM, vM] = cond2sp(A, Cp [, itermax, eps, verb])
Translated and reviewed by Rui Hirokawa
8897 of 132 results

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