In many applications, the coefficient matrix is symmetric and positive definite. The reason for this is usually that the partial differential operator from which it is derived is self-adjoint, coercive, and bounded (see Axelsson and Barker [.2]AxBa:febook). It follows that for the coefficient matrix the following relation holds for any matrix from a similar differential equation:

where , do not depend on the matrix size. The importance of this is that the use of as a preconditioner gives an iterative method with a number of iterations that does not depend on the matrix size.

Thus we can precondition our original matrix by one derived from a
different PDE, if one can be found that has attractive properties as
preconditioner.
The most common choice is to take a matrix from a *separable* PDE. A system involving such a matrix can be solved with
various so-called ``fast solvers'', such as FFT methods, cyclic
reduction, or the generalized marching algorithm
(see Dorr [75],
Swarztrauber [195], Bank [25] and
Bank and Rose [27]).

As a simplest example, any elliptic operator can be preconditioned with the Poisson operator, giving the iterative method

In Concus and Golub [59] a transformation of this method is considered to speed up the convergence. As another example, if the original matrix arises from

the preconditioner can be formed from

An extension to the non-self adjoint case is considered by Elman and Schultz [94].

Fast solvers are attractive in that the number of operations they require is (slightly higher than) of the order of the number of variables. Coupled with the fact that the number of iterations in the resulting preconditioned iterative methods is independent of the matrix size, such methods are close to optimal. However, fast solvers are usually only applicable if the physical domain is a rectangle or other Cartesian product structure. (For a domain consisting of a number of such pieces, domain decomposition methods can be used; see §).

Mon Nov 20 08:52:54 EST 1995