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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 : be a square system of ''n'' linear equations, where: Then ''A'' can be decomposed into a diagonal component ''D'', and the remainder ''R'': : The solution is then obtained iteratively via : where is the ''k''th approximation or iteration of and is the next or ''k'' + 1 iteration of . The element-based formula is thus: : 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)』 ■ウィキペディアで「Jacobi method」の詳細全文を読む スポンサード リンク
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