The above transformation of the linear system
is often not what is used in practice. For instance, the matrix
is
not symmetric, so, even if
and
are, the conjugate gradients
method is not immediately applicable to this system. The method as
described in figure
remedies this by employing the
-inner product for orthogonalization of the residuals. The
theory of the cg method is then applicable again.
All cg-type methods in this book, with the exception of QMR, have been derived with such a combination of preconditioned iteration matrix and correspondingly changed inner product.
Another way of deriving the preconditioned conjugate gradients method
would be to split the preconditioner as and
to transform the system as
If is symmetric and
, it is obvious that we now have a
method with a symmetric iteration matrix, hence the conjugate
gradients method can be applied.
Remarkably, the splitting of is in practice not needed.
By rewriting the steps of the method (see for
instance Axelsson and
Barker [pgs. 16,29]AxBa:febook or Golub and
Van Loan [.3]GoVL:matcomp) it
is usually possible to reintroduce a computational step
that is, a step that applies the preconditioner in its entirety.
There is a different approach to preconditioning, which is much easier to derive. Consider again the system.
The matrices and
are called the left- and right preconditioners , respectively, and we can simply apply an
unpreconditioned iterative method to this system. Only two additional
actions
before the iterative process and
after are necessary.
Thus we arrive at the following schematic for deriving a left/right preconditioned iterative method from any of the symmetrically preconditioned methods in this book.
where is the final calculated solution.