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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 .
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.