Previous: Reduced System Preconditioning
Up: Remaining topics
Next: Multigrid Methods
Previous Page: Reduced System Preconditioning
Next Page: Overlapping Subdomain Methods
In recent years, much attention has been given to domain decomposition
methods for linear elliptic problems that are based on a partitioning
of the domain of the physical problem. Since the subdomains can be
handled independently, such methods are very attractive for
coarse-grain parallel computers. On the other hand, it should be
stressed that they can be very effective even on sequential computers.
In this brief survey, we shall restrict ourselves to the standard
second order self-adjoint scalar elliptic problems in two dimensions
of the form:
where 
is a positive function on the domain 
, on whose
boundary the value of 
is prescribed (the Dirichlet problem). For
more general problems, and a good set of references, the reader is
referred to the series of
proceedings [172][135][106][48][47][46].
For the discretization of (
), we shall assume for
simplicity that is triangulated by a set 
of nonoverlapping
coarse triangles (subdomains) 
with 
internal
vertices. The 
's are in turn
further refined into a set of smaller triangles 
with n internal vertices in total.
Here 
denote the coarse and fine mesh size respectively.
By a standard Ritz-Galerkin method using piecewise linear triangular
basis elements on (), we obtain an 
symmetric positive definite linear system
.
Generally, there are two kinds of approaches depending on whether
the subdomains overlap with one another
(Schwarz methods) or are separated from
one another by interfaces (Schur Complement methods,
iterative substructuring).
We shall present domain decomposition methods as preconditioners
for the linear system
or to
a reduced (Schur Complement) system
defined on the interfaces in the non-overlapping formulation.
When used with the standard Krylov subspace methods discussed
elsewhere in this book, the user has to supply a procedure
for computing 
or 
given 
or 
and the algorithms to be described
herein computes 
.
The computation of is a simple sparse matrix-vector
multiply, but may require subdomain solves, as will be described later.
Previous: Reduced System Preconditioning
Up: Remaining topics
Next: Multigrid Methods
Previous Page: Reduced System Preconditioning
Next Page: Overlapping Subdomain Methods