The SOR node (

) handles settings for the SOR (successive overrelaxation) iterative method. Right-click the
Iterative,
Krylov Preconditioner,
Presmoother,
Postsmoother, or
Coarse Solver attribute nodes to add an
SOR node.
See The SOR Method for more detailed information about this feature.
Use the Solver list to specify which variant of the SOR algorithm to use. Select:
Enter the Number of iterations to perform when this node is used as a preconditioner or smoother. This setting is not considered when the attribute is used as a linear system solver (with the
Use preconditioner option in the
Solver list of the
Iterative attribute node). The solver then iterates until the relative tolerance specified by the corresponding operation node is fulfilled rather than performing a fixed number of iterations.
If this node is used with a Coarse Solver, select a
Termination technique to determine how to terminate the solver. Select
Fixed number of iterations to perform a fixed number of iterations each time the
Coarse Solver is used, or
Use tolerance to terminate the
Coarse Solver when a tolerance is fulfilled.
If Fixed number of iterations is selected, enter a value for the
Number of iterations to perform. The default is 10.
If Use tolerance is selected, enter a value for each of the following:
Specify a scalar Relaxation factor ω. The allowed values of this factor are between 0 and 2 (default: 1). See
About the Relaxation Factor for more information.
The Blocked version checkbox is selected by default and it uses a blocked version of the SOR method that is optimized for parallel computations.
M is then constructed from a column-permuted version of
A.
Select the Reuse data checkbox (selected by default) to reuse the data in the blocks that define the SOR method. If you have selected that checkbox, the
Reuse sparsity pattern checkbox is available. It is elected by default to store the sparsity patterns of the assembled matrices and try to reuse them for successive assembly processes within the same solution process. In many cases, the sparsity pattern of the system matrices does not change from one nonlinear iteration or time step to the others. Reusing the sparsity pattern from the previous iteration or step can then improve the solution performance at the cost of a usually small amount of memory.