Numerical analysis Iterative methods for linear systems Theorem
Balancing domain decomposition
Builds on Schur complement methodSchur complementPreconditionerCondition numberFinite element methodElliptic partial differential equation
Statement
Let , , be a polygonal or polyhedral domain. Divide it into non-overlapping subdomains of diameter about , triangulated by a quasi-uniform mesh of size . Let be constant on each . Consider the finite element discretization of by continuous piecewise linear functions, with homogeneous Dirichlet conditions on part of . Let be the interface Schur complement, where restricts interface values to and is the local Schur complement of . Let be diagonal weight matrices with . Let have columns spanning the null space of (the constants for a floating subdomain), and let have the columns . With , the balancing domain decomposition preconditioner is where is a generalized inverse of . Choose the weights from the values of as in the standard theory. Then the condition number of satisfies with independent of , , and the values of .
Remarks
The local operator is the Neumann–Neumann preconditioner: each solves a Neumann problem on . A Neumann problem on a subdomain that does not touch the Dirichlet boundary is solvable only if its right-hand side is orthogonal to the constants. The coarse correction "balances" the residual so that these compatibility conditions hold, and at the same time it passes global information between all subdomains. The proof bounds the energy of the weighted average of local functions by trace and discrete harmonic extension estimates, which bring in the factor .
The bound depends on only, so refining the mesh and adding subdomains in proportion keeps the number of conjugate gradient iterations nearly constant. The method was introduced by Jan Mandel in 1993.
Going deeper
For a student
The plain Neumann–Neumann method has no coarse space and scales poorly with the number of subdomains. BDD adds a coarse space, and the later BDDC method uses a different coarse space built from primal constraints. Its condition number bound is of the same polylogarithmic type as that of FETI methods, and in fact the two families are closely related. It is one of the domain decomposition methods that are robust with respect to large jumps of the coefficient across subdomains.
Reading path
To understand this page from the ground up, read these 9 pages in order. The foundations come first.
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- no evidence level
- Block matrix concept
- Condition number concept
- Elliptic partial differential equation concept
- Finite element method concept
- Positive definite matrix concept
- Preconditioner concept
- Schur complement concept
- Schur complement method statement
- Balancing domain decomposition statement
Further reading
About this page
| Tag | X8MCVW56QP |
|---|---|
| URI | m2://X8MCVW56QP |
| Kind | statement, theorem |
| Writing | W0 stub |
| MSC | 65N55, 65F08, 65F10 |
| Status | This copy of the site has no build data. Run m2 status X8MCVW56QP. |
| About | Domain decomposition methods, Preconditioner |
| Uses | Schur complement method, Schur complement, Preconditioner, Condition number, Finite element method, Elliptic partial differential equation |
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