!- Converted with LaTeX2HTML 0.6.4 (Tues Aug 30 1994) by Nikos Drakos (firstname.lastname@example.org), CBLU, University of Leeds ->
The Generalized Minimal Residual method  is an extension of MINRES (which is only applicable to symmetric systems) to unsymmetric systems. Like MINRES, it generates a sequence of orthogonal vectors, but in the absence of symmetry this can no longer be done with short recurrences; instead, all previously computed vectors in the orthogonal sequence have to be retained. For this reason, ``restarted '' versions of the method are used.
In the Conjugate Gradient method, the residuals form an orthogonal basis for the space . In GMRES, this basis is formed explicitly:
The reader may recognize this as a modified Gram-Schmidt orthogonalization. Applied to the Krylov sequence this orthogonalization is called the ``Arnoldi method'' . The inner product coefficients and are stored in an upper Hessenberg matrix.
The GMRES iterates are constructed as
where the coefficients have been chosen to minimize the residual norm . The GMRES algorithm has the property that this residual norm can be computed without the iterate having been formed. Thus, the expensive action of forming the iterate can be postponed until the residual norm is deemed small enough.
The pseudocode for the restarted GMRES() algorithm with preconditioner is given in Figure .
Figure: The Preconditioned GMRES Method