The conjugate residual method is an iterative numeric method used for solving systems of linear equations. It's a Krylov subspace method very similar to the much more popular conjugate gradient method, with similar construction and convergence properties.
This method is used to solve linear equations of the form
A x = b {\displaystyle \mathbf {A} \mathbf {x} =\mathbf {b} }where A is an invertible and Hermitian matrix, and b is nonzero.
The conjugate residual method differs from the closely related conjugate gradient method. It involves more numerical operations and requires more storage.
Given an (arbitrary) initial estimate of the solution x 0 {\displaystyle \mathbf {x} _{0}} , the method is outlined below:
x 0 := Some initial guess r 0 := b − A x 0 p 0 := r 0 Iterate, with k starting at 0 : α k := r k T A r k ( A p k ) T A p k x k + 1 := x k + α k p k r k + 1 := r k − α k A p k β k := r k + 1 T A r k + 1 r k T A r k p k + 1 := r k + 1 + β k p k A p k + 1 := A r k + 1 + β k A p k k := k + 1 {\displaystyle {\begin{aligned}&\mathbf {x} _{0}:={\text{Some initial guess}}\\&\mathbf {r} _{0}:=\mathbf {b} -\mathbf {Ax} _{0}\\&\mathbf {p} _{0}:=\mathbf {r} _{0}\\&{\text{Iterate, with }}k{\text{ starting at }}0:\\&\qquad \alpha _{k}:={\frac {\mathbf {r} _{k}^{\mathrm {T} }\mathbf {Ar} _{k}}{(\mathbf {Ap} _{k})^{\mathrm {T} }\mathbf {Ap} _{k}}}\\&\qquad \mathbf {x} _{k+1}:=\mathbf {x} _{k}+\alpha _{k}\mathbf {p} _{k}\\&\qquad \mathbf {r} _{k+1}:=\mathbf {r} _{k}-\alpha _{k}\mathbf {Ap} _{k}\\&\qquad \beta _{k}:={\frac {\mathbf {r} _{k+1}^{\mathrm {T} }\mathbf {Ar} _{k+1}}{\mathbf {r} _{k}^{\mathrm {T} }\mathbf {Ar} _{k}}}\\&\qquad \mathbf {p} _{k+1}:=\mathbf {r} _{k+1}+\beta _{k}\mathbf {p} _{k}\\&\qquad \mathbf {Ap} _{k+1}:=\mathbf {Ar} _{k+1}+\beta _{k}\mathbf {Ap} _{k}\\&\qquad k:=k+1\end{aligned}}}the iteration may be stopped once x k {\displaystyle \mathbf {x} _{k}} has been deemed converged. The only difference between this and the conjugate gradient method is the calculation of α k {\displaystyle \alpha _{k}} and β k {\displaystyle \beta _{k}} (plus the optional incremental calculation of A p k {\displaystyle \mathbf {Ap} _{k}} at the end).
Note: the above algorithm can be transformed so to make only one symmetric matrix-vector multiplication in each iteration.
Preconditioning
By making a few substitutions and variable changes, a preconditioned conjugate residual method may be derived in the same way as done for the conjugate gradient method:
x 0 := Some initial guess r 0 := M − 1 ( b − A x 0 ) p 0 := r 0 Iterate, with k starting at 0 : α k := r k T A r k ( A p k ) T M − 1 A p k x k + 1 := x k + α k p k r k + 1 := r k − α k M − 1 A p k β k := r k + 1 T A r k + 1 r k T A r k p k + 1 := r k + 1 + β k p k A p k + 1 := A r k + 1 + β k A p k k := k + 1 {\displaystyle {\begin{aligned}&\mathbf {x} _{0}:={\text{Some initial guess}}\\&\mathbf {r} _{0}:=\mathbf {M} ^{-1}(\mathbf {b} -\mathbf {Ax} _{0})\\&\mathbf {p} _{0}:=\mathbf {r} _{0}\\&{\text{Iterate, with }}k{\text{ starting at }}0:\\&\qquad \alpha _{k}:={\frac {\mathbf {r} _{k}^{\mathrm {T} }\mathbf {A} \mathbf {r} _{k}}{(\mathbf {Ap} _{k})^{\mathrm {T} }\mathbf {M} ^{-1}\mathbf {Ap} _{k}}}\\&\qquad \mathbf {x} _{k+1}:=\mathbf {x} _{k}+\alpha _{k}\mathbf {p} _{k}\\&\qquad \mathbf {r} _{k+1}:=\mathbf {r} _{k}-\alpha _{k}\mathbf {M} ^{-1}\mathbf {Ap} _{k}\\&\qquad \beta _{k}:={\frac {\mathbf {r} _{k+1}^{\mathrm {T} }\mathbf {A} \mathbf {r} _{k+1}}{\mathbf {r} _{k}^{\mathrm {T} }\mathbf {A} \mathbf {r} _{k}}}\\&\qquad \mathbf {p} _{k+1}:=\mathbf {r} _{k+1}+\beta _{k}\mathbf {p} _{k}\\&\qquad \mathbf {Ap} _{k+1}:=\mathbf {A} \mathbf {r} _{k+1}+\beta _{k}\mathbf {Ap} _{k}\\&\qquad k:=k+1\\\end{aligned}}}The preconditioner M − 1 {\displaystyle \mathbf {M} ^{-1}} must be symmetric positive definite. Note that the residual vector here is different from the residual vector without preconditioning.
- Yousef Saad, Iterative methods for sparse linear systems (2nd ed.), page 194, SIAM. ISBN 978-0-89871-534-7.