| 作成者 |
|
|
|
|
|
| 本文言語 |
|
| 出版者 |
|
|
|
| 発行日 |
|
| 収録物名 |
|
| 巻 |
|
| 号 |
|
| 開始ページ |
|
| 終了ページ |
|
| 出版タイプ |
|
| アクセス権 |
|
| 関連DOI |
|
| 関連DOI |
|
| 関連URI |
|
| 関連URI |
|
| 関連HDL |
|
| 関連情報 |
|
| 概要 |
The balancing domain decomposition (BDD) method is a well-known preconditioner due to its excellent convergence rate. The BDD method includes the Neumann-Neumann preconditioner and a coarse grid corre...ction. Several studies have considered applications of the BDD method to various phenomena and improvement of its convergence rate. However, in applying the BDD method to large-scale problems, it is difficult to solve the coarse problem of a coarse grid correction since the size of the coarse problem increases in proportion to the number of subdomains (i.e., the size of the original problem). Other preconditioners with a coarse grid correction have the same problem. To overcome this problem, use of a new preconditioner, namely, incomplete balancing domain decomposition with a diagonal-scaling (IBDD-DIAG) method is proposed in this study. The method is based on the BDD method, and constructed by an incomplete balancing preconditioner and a simplified diagonal-scaling preconditioner. Moreover, it is parallelized by the hierarchical domain decomposition method. To evaluate this new approach, some computational examples of large-scale problems are demonstrated.続きを見る
|
Mendeley出力
jcst08_ogino_ibdd