A convergence acceleration method based on an additive correction multigrid -SIMPLEC (ACM-S) algorithm with dynamic tuning of the relaxation factors is presented. In the ACM-S method, the coarse grid velocity correction components obtained from the mass conservation (velocity potential) correction e
Convergence of Multigrid Algorithms for Interior Penalty Methods
β Scribed by Susanne C. Brenner; Jie Zhao
- Publisher
- John Wiley and Sons
- Year
- 2005
- Weight
- 179 KB
- Volume
- 2
- Category
- Article
- ISSN
- 1611-8170
No coin nor oath required. For personal study only.
β¦ Synopsis
V -cycle, F -cycle and W -cycle multigrid algorithms for interior penalty methods for second order elliptic boundary value problems are studied in this paper. It is shown that these algorithms converge uniformly with respect to all grid levels if the number of smoothing steps is sufficiently large, and that the contraction numbers decrease as the number of smoothing steps increases, at a rate determined by the elliptic regularity of the problem.
π SIMILAR VOLUMES
Multigrid methods for discretized partial differential problems using nonnested conforming and nonconforming finite elements are here defined in the general setting. The coarse-grid corrections of these multigrid methods make use of different finite element spaces from those on the finest grid. In g
The main purpose of the present paper is the study of computational aspects, ## Ε½ . and primarily the convergence rate, of genetic algorithms GAs . Despite the fact that such algorithms are widely used in practice, little is known so far about their theoretical properties, and in particular about
A multigrid scheme naturally contained in wa¨elet expansion methods is presented. Careful examination of the wa¨elet matrix re¨eals matrix representations of an integral operator at ¨arious coarse le¨els that can be identified as nested submatrices of the original wa¨elet matrix at the finest le¨el.
This paper considers the problem of dynamic errors-in-variables identification. Convergence properties of the previously proposed bias-eliminating algorithms are investigated. An error dynamic equation for the bias-eliminating parameter estimates is derived. It is shown that the convergence of the b