Name
Abstract | ||
|---|---|---|
We describe an efficient implementation and present a performance study of an automated multi-level substructuring (AMLS) method for sparse eigenvalue problems. We assess the time and memory requirements associated with the key steps of the algorithm, and compare it with the shift-and-invert Lanczos algorithm. Our eigenvalue problems come from two very different application areas: accelerator cavity design and normal-mode vibrational analysis of polyethylene particles. We show that the AMLS method, when implemented carefully, outperforms the traditional method in broad application areas when large numbers of eigenvalues are sought, with relatively low accuracy. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1377596.1377600 | ACM Transactions on Mathematical Software |
Keywords | DocType | Volume |
traditional method,amls method,sparse eigenvalue problem,sparse eigenvalue problems,shift-and-invert lanczos algorithm,accelerator cavity design,different application area,automated multi-level substructuring,efficient implementation,broad application area,performance evaluation,multilevel substructuring,eigenvalue problem | Journal | 34 |
Issue | ISSN | Citations |
4 | 0098-3500 | 3 |
PageRank | References | Authors |
0.43 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiguo Gao | 1 | 42 | 5.94 |
| Xiaoye S. Li | 2 | 1042 | 98.22 |
| Chao Yang | 3 | 180 | 18.36 |
| Zhaojun Bai | 4 | 661 | 107.69 |