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biconjugate gradient method : ウィキペディア英語版
biconjugate gradient method

In mathematics, more specifically in numerical linear algebra, the biconjugate gradient method is an algorithm to solve systems of linear equations
:A x= b.\,
Unlike the conjugate gradient method, this algorithm does not require the matrix A to be self-adjoint, but instead one needs to perform multiplications by the conjugate transpose .
==The algorithm==

# Choose initial guess x_0\,, two other vectors x_0^
* and b^
*\, and a preconditioner M\,
# r_0 \leftarrow b-A\, x_0\,
# r_0^
* \leftarrow b^
*-x_0^
*\, A^T
# p_0 \leftarrow M^ r_0\,
# p_0^
* \leftarrow r_0^
*M^\,
# for k=0, 1, \ldots do
## \alpha_k \leftarrow \,
## x_ \leftarrow x_k + \alpha_k \cdot p_k\,
## x_^
* \leftarrow x_k^
* + \overline\cdot p_k^
*\,
## r_ \leftarrow r_k - \alpha_k \cdot A p_k\,
## r_^
* \leftarrow r_k^
*- \overline \cdot p_k^
*\, A
## \beta_k \leftarrow r_ \over r_k^
* M^ r_k}\,
## p_ \leftarrow M^ r_ + \beta_k \cdot p_k\,
## p_^
* \leftarrow r_^
*M^ + \overline\cdot p_k^
*\,
In the above formulation, the computed r_k\, and r_k^
* satisfy
:r_k = b - A x_k,\,
:r_k^
* = b^
* - x_k^
*\, A
and thus are the respective residuals corresponding to x_k\, and x_k^
*, as approximate solutions to the systems
:A x = b,\,
:x^
*\, A = b^
*\,;
x^
* is the adjoint, and \overline is the complex conjugate.

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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