nonsymmetric dynamics

now compute the preconditioner
MLPrec->ComputePreconditioner();
// solve the linear system
...
// destroy the previously define preconditioner, and build a new one
MLPrec->DestroyPreconditioner();
// re-compute the elements of A
// now re-compute the preconditioner, using either
MLPrec->ComputePreconditioner();
// or
MLPrec->ReComputePreconditioner();
// re-solve the linear system
// .. finally destroy the object
delete MLPrec;
In this fragment of code, the user defines the ML preconditioner, but the preconditioner
is created only with the call ComputePreconditioner(). This may be useful, for example,
when ML is used in conjunction with nonlinear solvers (like Nox [17]). Method
ReComputePreconditioner() can be used to recompute the preconditioner using already
available information about the aggregates. ReComputePreconditioner() reuses the already
computed tentative prolongator, then recomputes the smoothed prolongators and the
other components of the hierarchy, like smoothers and coarse solver 11 .
6.2 Example 2: ml 2level DD.cpp
In the second example, a two level domain decomposition preconditioner is constructed.
The coarse space is defined using aggregation. It is worthwhile to compare the parameters
selected here to the default parameters stated in Table 6.
File $ML HOME/examples/TwoLevelDD/ml 2level DD.cpp reports the entire code. In
the example, the linear system matrix A, is an Epetra CrsMatrix corresponding to the
discretization of a Laplacian on a 2D Cartesian grid. The solution vector and right-hand
side are x and b respectively.
The AztecOO linear problem is defined as
Epetra_LinearProblem problem(&A, &x, &b);
AztecOO solver(problem);
We create the Teuchos parameter list as follows:
ParameterList MLList;
ML_Epetra::SetDefaults("DD", MLList);
MLList.set("max levels",2);
11 Note that the hierarchy produced by ReComputePreconditioner() can differ from that produced by
ComputePreconditioner() for non-zero threshold values.
16