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Jacobi method
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Jacobi method : ウィキペディア英語版
Jacobi method
In numerical linear algebra, the Jacobi method (or Jacobi iterative method) is an algorithm for determining the solutions of a diagonally dominant system of linear equations. Each diagonal element is solved for, and an approximate value is plugged in. The process is then iterated until it converges. This algorithm is a stripped-down version of the Jacobi transformation method of matrix diagonalization. The method is named after Carl Gustav Jacob Jacobi.
== Description ==
Let
:A\mathbf x = \mathbf b
be a square system of ''n'' linear equations, where:
A=\begin a_ & a_ & \cdots & a_ \\ a_ & a_ & \cdots & a_ \\ \vdots & \vdots & \ddots & \vdots \\a_ & a_ & \cdots & a_ \end, \qquad \mathbf = \begin x_ \\ x_2 \\ \vdots \\ x_n \end , \qquad \mathbf = \begin b_ \\ b_2 \\ \vdots \\ b_n \end.
Then ''A'' can be decomposed into a diagonal component ''D'', and the remainder ''R'':
:A=D+R \qquad \text \qquad D = \begin a_ & 0 & \cdots & 0 \\ 0 & a_ & \cdots & 0 \\ \vdots & \vdots & \ddots & \vdots \\0 & 0 & \cdots & a_ \end \text R = \begin 0 & a_ & \cdots & a_ \\ a_ & 0 & \cdots & a_ \\ \vdots & \vdots & \ddots & \vdots \\ a_ & a_ & \cdots & 0 \end.
The solution is then obtained iteratively via
: \mathbf^ = D^ (\mathbf - R \mathbf^),
where \mathbf^ is the ''k''th approximation or iteration of \mathbf and \mathbf^ is the next or ''k'' + 1 iteration of \mathbf. The element-based formula is thus:
: x^_i = \fraca_x^_j\right),\quad i=1,2,\ldots,n.
The computation of ''x''''i''(''k''+1) requires each element in x(''k'') except itself. Unlike the Gauss–Seidel method, we can't overwrite ''x''''i''(''k'') with ''x''''i''(''k''+1), as that value will be needed by the rest of the computation. The minimum amount of storage is two vectors of size ''n''.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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