Up: Generalized Minimal Residual (GMRES)
Previous Page: Theory
Next Page: BiConjugate Gradient (BiCG)
A common implementation of GMRES is suggested by Saad and Schultz in  and relies on using modified Gram-Schmidt orthogonalization. Householder transformations, which are relatively costly but stable, have also been proposed. The Householder approach results in a three-fold increase in work; however, convergence may be better, especially for ill-conditioned systems (see Walker ). From the point of view of parallelism, Gram-Schmidt orthogonalization may be preferred, giving up some stability for better parallelization properties (see Demmel, Heath and Van der Vorst ). Here we adopt the Modified Gram-Schmidt approach.
The major drawback to GMRES is that the amount of work and storage required per iteration rises linearly with the iteration count. Unless one is fortunate enough to obtain extremely fast convergence, the cost will rapidly become prohibitive. The usual way to overcome this limitation is by restarting the iteration. After a chosen number (m) of iterations, the accumulated data are cleared and the intermediate results are used as the initial data for the next m iterations. This procedure is repeated until convergence is achieved. The difficulty is in choosing an appropriate value for m. If m is ``too small'', GMRES(m) may be slow to converge, or fail to converge entirely. A value of m that is larger than necessary involves excessive work (and uses more storage). Unfortunately, there are no definite rules governing the choice of m---choosing when to restart is a matter of experience.
For a discussion of GMRES for vector and shared memory computers see Dongarra et al. ; for more general architectures, see Demmel, Heath and Van der Vorst .