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| {{no footnotes|date=September 2013|talk=yes}}
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| In [[mathematics]], more specifically in [[numerical linear algebra]], the '''biconjugate gradient method''' is an [[algorithm]] to solve [[system of linear equations|systems of linear equations]]
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| :<math>A x= b.\,</math>
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| Unlike the [[conjugate gradient method]], this algorithm does not require the [[matrix (mathematics)|matrix]] <math>A</math> to be [[self-adjoint]], but instead one needs to perform multiplications by the [[conjugate transpose]] {{math|<var>A</var><sup>*</sup>}}.
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| ==The algorithm==
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| # Choose initial guess <math>x_0\,</math>, two other vectors <math>x_0^*</math> and <math>b^*\,</math> and a [[preconditioner]] <math>M\,</math>
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| # <math>r_0 \leftarrow b-A\, x_0\,</math>
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| # <math>r_0^* \leftarrow b^*-x_0^*\, A^T </math>
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| # <math>p_0 \leftarrow M^{-1} r_0\,</math>
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| # <math>p_0^* \leftarrow r_0^*M^{-1}\,</math>
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| # for <math>k=0, 1, \ldots</math> do
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| ## <math>\alpha_k \leftarrow {r_k^* M^{-1} r_k \over p_k^* A p_k}\,</math>
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| ## <math>x_{k+1} \leftarrow x_k + \alpha_k \cdot p_k\,</math>
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| ## <math>x_{k+1}^* \leftarrow x_k^* + \overline{\alpha_k}\cdot p_k^*\,</math>
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| ## <math>r_{k+1} \leftarrow r_k - \alpha_k \cdot A p_k\,</math>
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| ## <math>r_{k+1}^* \leftarrow r_k^*- \overline{\alpha_k} \cdot p_k^*\, A </math>
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| ## <math>\beta_k \leftarrow {r_{k+1}^* M^{-1} r_{k+1} \over r_k^* M^{-1} r_k}\,</math>
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| ## <math>p_{k+1} \leftarrow M^{-1} r_{k+1} + \beta_k \cdot p_k\,</math>
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| ## <math>p_{k+1}^* \leftarrow r_{k+1}^*M^{-1} + \overline{\beta_k}\cdot p_k^*\,</math>
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| In the above formulation, the computed <math>r_k\,</math> and <math>r_k^*</math> satisfy
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| :<math>r_k = b - A x_k,\,</math>
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| :<math>r_k^* = b^* - x_k^*\, A </math>
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| and thus are the respective [[Residual (numerical analysis)|residual]]s corresponding to <math>x_k\,</math> and <math>x_k^*</math>, as approximate solutions to the systems
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| :<math>A x = b,\,</math>
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| :<math>x^*\, A = b^*\,;</math>
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| <math>x^*</math> is the [[Hermitian adjoint|adjoint]], and <math>\overline{\alpha}</math> is the [[complex conjugate]].
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| === Unpreconditioned version of the algorithm ===
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| # Choose initial guess <math>x_0\,</math>,
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| # <math>r_0 \leftarrow b-A\, x_0\,</math>
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| # <math>\hat{r}_0 \leftarrow \hat{b} - \hat{x}_0A^T </math>
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| # <math>p_0 \leftarrow r_0\,</math>
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| # <math>\hat{p}_0 \leftarrow \hat{r}_0\,</math>
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| # for <math>k=0, 1, \ldots</math> do
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| ## <math>\alpha_k \leftarrow {\hat{r}_k r_k \over \hat{p}_k A p_k}\,</math>
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| ## <math>x_{k+1} \leftarrow x_k + \alpha_k \cdot p_k\,</math>
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| ## <math>\hat{x}_{k+1} \leftarrow \hat{x}_k + \alpha_k \cdot \hat{p}_k\,</math>
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| ## <math>r_{k+1} \leftarrow r_k - \alpha_k \cdot A p_k\,</math>
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| ## <math>\hat{r}_{k+1} \leftarrow \hat{r}_k- \alpha_k \cdot \hat{p}_k A^T </math>
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| ## <math>\beta_k \leftarrow {\hat{r}_{k+1} r_{k+1} \over \hat{r}_k r_k}\,</math>
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| ## <math>p_{k+1} \leftarrow r_{k+1} + \beta_k \cdot p_k\,</math>
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| ## <math>\hat{p}_{k+1} \leftarrow \hat{r}_{k+1} + \beta_k \cdot \hat{p}_k\,</math>
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| ==Discussion==
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| The biconjugate gradient method is [[numerical stability|numerically unstable]]{{Citation needed|date=September 2009}} (compare to the [[biconjugate gradient stabilized method]]), but very important from a theoretical point of view. Define the iteration steps by
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| :<math>x_k:=x_j+ P_k A^{-1}\left(b - A x_j \right),</math>
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| :<math>x_k^*:= x_j^*+\left(b^*- x_j^* A \right) P_k A^{-1},</math>
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| where <math>j<k</math> using the related [[projection (linear algebra)|projection]]
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| :<math>P_k:= \mathbf{u}_k \left(\mathbf{v}_k^* A \mathbf{u}_k \right)^{-1} \mathbf{v}_k^* A,</math>
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| with
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| :<math>\mathbf{u}_k=\left[u_0, u_1, \dots, u_{k-1} \right],</math>
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| :<math>\mathbf{v}_k=\left[v_0, v_1, \dots, v_{k-1} \right].</math>
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| These related projections may be iterated themselves as
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| :<math>P_{k+1}= P_k+ \left( 1-P_k\right) u_k \otimes {v_k^* A\left(1-P_k \right) \over v_k^* A\left(1-P_k \right) u_k}.</math>
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| A relation to [[Quasi-Newton method]]s is given by <math>P_k= A_k^{-1} A</math> and <math>x_{k+1}= x_k- A_{k+1}^{-1}\left(A x_k -b \right)</math>, where
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| :<math>A_{k+1}^{-1}= A_k^{-1}+ \left( 1-A_k^{-1}A\right) u_k \otimes {v_k^* \left(1-A A_k^{-1} \right) \over v_k^* A\left(1-A_k^{-1}A \right) u_k}.</math>
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| The new directions
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| :<math>p_k = \left(1-P_k \right) u_k,</math>
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| :<math>p_k^* = v_k^* A \left(1- P_k \right) A^{-1}</math>
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| are then orthogonal to the residuals:
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| :<math>v_i^* r_k= p_i^* r_k=0,</math>
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| :<math>r_k^* u_j = r_k^* p_j= 0,</math>
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| which themselves satisfy
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| :<math>r_k= A \left( 1- P_k \right) A^{-1} r_j,</math>
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| :<math>r_k^*= r_j^* \left( 1- P_k \right)</math>
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| where <math>i,j<k</math>.
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| The biconjugate gradient method now makes a special choice and uses the setting
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| :<math>u_k = M^{-1} r_k,\,</math>
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| :<math>v_k^* = r_k^* \, M^{-1}.\,</math>
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| With this particular choice, explicit evaluations of <math>P_k</math> and {{math|<var>A</var><sup>−1</sup>}} are avoided, and the algorithm takes the form stated above.
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| ==Properties==
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| * If <math>A= A^*\,</math> is [[Conjugate transpose|self-adjoint]], <math>x_0^*= x_0</math> and <math>b^*=b</math>, then <math>r_k= r_k^*</math>, <math>p_k= p_k^*</math>, and the [[conjugate gradient method]] produces the same sequence <math>x_k= x_k^*</math> at half the computational cost.
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| * The sequences produced by the algorithm are [[Biorthogonal system|biorthogonal]], i.e., <math>p_i^*Ap_j=r_i^*M^{-1}r_j=0</math> for <math>i \neq j</math>.
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| * if <math>P_{j'}\,</math> is a polynomial with <math>\mathrm{deg}\left(P_{j'}\right)+j<k</math>, then <math>r_k^*P_{j'}\left(M^{-1}A\right)u_j=0</math>. The algorithm thus produces projections onto the [[Krylov subspace]].
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| * if <math>P_{i'}\,</math> is a polynomial with <math>i+\mathrm{deg}\left(P_{i'}\right)<k</math>, then <math>v_i^*P_{i'}\left(AM^{-1}\right)r_k=0</math>.
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| ==See also==
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| * [[Biconjugate gradient stabilized method]]
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| * [[Conjugate gradient method]]
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| ==References==
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| * {{cite journal|first=R.|last=Fletcher|year=1976|title=Conjugate gradient methods for indefinite systems|journal=Numerical Analysis|volume=506|series=Lecture Notes in Mathematics|publisher=Springer Berlin / Heidelberg|issn=1617-9692|isbn=978-3-540-07610-0|pages=73–89|url=http://www.springerlink.com/content/974t1l33m84217um/|doi=10.1007/BFb0080109|editor1-last=Watson|editor1-first=G. Alistair}}
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| * {{Cite book |last1=Press|first1=WH|last2=Teukolsky|first2=SA|last3=Vetterling|first3=WT|last4=Flannery|first4=BP|year=2007|title=Numerical Recipes: The Art of Scientific Computing|edition=3rd|publisher=Cambridge University Press| publication-place=New York|isbn=978-0-521-88068-8|chapter=Section 2.7.6|chapter-url=http://apps.nrbook.com/empanel/index.html?pg=87 |postscript=<!-- Bot inserted parameter. Either remove it; or change its value to "." for the cite to end in a ".", as necessary. -->{{inconsistent citations}}}}
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| {{Numerical linear algebra}}
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| [[Category:Numerical linear algebra]]
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| [[Category:Gradient methods]]
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