Conjugate residual method

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

$\mathbf {A} \mathbf {x} =\mathbf {b}$

where A is an invertible and Hermitian matrix, and b is nonzero.

The conjugate residual method differs from the closely related conjugate gradient method primarily in that it involves more numerical operations and requires more storage, but the system matrix may be merely semi-definite. The conjugate residual method then computes the least-square solution of the linear problem.

Given an (arbitrary) initial estimate of the solution $\mathbf {x} _{0}$, the method is outlined below:

$\mathbf {x} _{0}={\text{Some initial guess}}\,$

$\mathbf {r} _{0}=\mathbf {b} -\mathbf {Ax} _{0}\,$

$\mathbf {p} _{0}=\mathbf {r} _{0}\,$

${\text{Iterate, with }}k{\text{ starting at }}0:$

$\alpha _{k}={\frac {\mathbf {r} _{k}^{\mathrm {T} }\mathbf {Ar} _{k}}{(\mathbf {Ap} _{k})^{\mathrm {T} }\mathbf {Ap} _{k}}}\,$

$\mathbf {x} _{k+1}=\mathbf {x} _{k}+\alpha _{k}\mathbf {p} _{k}\,$

$\mathbf {r} _{k+1}=\mathbf {r} _{k}-\alpha _{k}\mathbf {Ap} _{k}\,$

$\beta _{k}={\frac {\mathbf {r} _{k+1}^{\mathrm {T} }\mathbf {Ar} _{k+1}}{\mathbf {r} _{k}^{\mathrm {T} }\mathbf {Ar} _{k}}}\,$

$\mathbf {p} _{k+1}=\mathbf {r} _{k+1}+\beta _{k}\mathbf {p} _{k}\,$

$\mathbf {Ap} _{k+1}=\mathbf {Ar} _{k+1}+\beta _{k}\mathbf {Ap} _{k}\,$

$k:=k+1\,$

the iteration may be stopped once $\mathbf {x} _{k}$ has been deemed converged. Note that the only difference between this and the conjugate gradient method is the calculation of $\alpha _{k}$ and $\beta _{k}$ (plus the optional incremental calculation of $\mathbf {Ap} _{k}$ at the end).

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:

$\mathbf {x} _{0}={\text{Some initial guess}}\,$

$\mathbf {r} _{0}=\mathbf {M} ^{-1}(\mathbf {b} -\mathbf {Ax} _{0})\,$

$\mathbf {p} _{0}=\mathbf {r} _{0}\,$

${\text{Iterate, with }}k{\text{ starting at }}0:$

$\alpha _{k}={\frac {\mathbf {r} _{k}^{\mathrm {T} }\mathbf {A} \mathbf {r} _{k}}{(\mathbf {Ap} _{k})^{\mathrm {T} }\mathbf {M} ^{-1}\mathbf {Ap} _{k}}}\,$

$\mathbf {x} _{k+1}=\mathbf {x} _{k}+\alpha _{k}\mathbf {p} _{k}\,$

$\mathbf {r} _{k+1}=\mathbf {r} _{k}-\alpha _{k}\mathbf {M} ^{-1}\mathbf {Ap} _{k}\,$

$\beta _{k}={\frac {\mathbf {r} _{k+1}^{\mathrm {T} }\mathbf {A} \mathbf {r} _{k+1}}{\mathbf {r} _{k}^{\mathrm {T} }\mathbf {A} \mathbf {r} _{k}}}\,$

$\mathbf {p} _{k+1}=\mathbf {r} _{k+1}+\beta _{k}\mathbf {p} _{k}\,$

$\mathbf {Ap} _{k+1}=\mathbf {A} \mathbf {r} _{k+1}+\beta _{k}\mathbf {Ap} _{k}\,$

$k:=k+1\,$

The preconditioner $\mathbf {M} ^{-1}$ must be symmetric. Note that the residual vector here is different from the residual vector without preconditioning.

References

• Yousef Saad, Iterative methods for sparse linear systems (2nd ed.), page 194, SIAM. ISBN 978-0-89871-534-7.
• Jonathan Richard Shewchuck, An Introduction to the Conjugate Gradient Method Without the Agonizing Pain (edition $1{\tfrac {1}{4}}$), pages 39–40.