This paper analyses the effects of inaccurate linear solvers on the behaviour of inverse iteration and Rayleigh quotient iteration. We derive an expression for the worst-case perturbation of the convergence factor of the exact iteration, due to the inexact solution. A necessary and sufficient condi
On the use of inexact subdomain solvers for BDDC algorithms
โ Scribed by Jing Li; Olof B. Widlund
- Publisher
- Elsevier Science
- Year
- 2007
- Tongue
- English
- Weight
- 322 KB
- Volume
- 196
- Category
- Article
- ISSN
- 0045-7825
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โฆ Synopsis
The standard BDDC (balancing domain decomposition by constraints) preconditioner is shown to be equivalent to a preconditioner built from a partially subassembled finite element model. This results in a system of linear algebraic equations which is much easier to solve in parallel than the fully assembled model; the cost is then often dominated by that of the problems on the subdomains. An important role is also played, both in theory and practice, by an averaging operator and in addition exact Dirichlet solvers are used on the subdomains in order to eliminate the residual in the interior of the subdomains. The use of inexact solvers for these problems and even the replacement of the Dirichlet solvers by a trivial extension are considered. It is established that one of the resulting algorithms has the same eigenvalues as the standard BDDC algorithm, and the connection of another with the FETI-DP algorithm with a lumped preconditioner is also considered. Multigrid methods are used in the experimental work and under certain assumptions, it is established that the iteration count essentially remains the same as when exact solvers are used, while considerable gains in the speed of the algorithm can be realized since the cost of the exact solvers grows superlinearly with the size of the subdomain problems while the multigrid methods are linear.
๐ SIMILAR VOLUMES
In this paper we introduce general iterative methods for finding zeros of a maximal monotone operator in a Hilbert space which unify two previously studied iterative methods: relaxed proximal point algorithm [H.K. Xu, Iterative algorithms for nonlinear operators, J. London Math Soc. 66 (2002) 240-25