Name
Abstract | ||
|---|---|---|
This work presents an adaptive block Lanczos method for large-scale non-Hermitian Eigenvalue problems (henceforth the ABLE method). The ABLE method is a block version of the non-Hermitian Lanczos algorithm. There are three innovations. First, an adaptive blocksize scheme cures (near) breakdown and adapts the blocksize to the order of multiple or clustered eigenvalues. Second, stopping criteria are developed that exploit the semiquadratic convergence property of the method. Third, a well-known technique from the Hermitian Lanczos algorithm is generalized to monitor the loss of biorthogonality and maintain semibiorthogonality among the computed Lanczos vectors. Each innovation is theoretically justified. Academic model problems and real application problems are solved to demonstrate the numerical behaviors of the method. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1137/S0895479897317806 | SIAM J. Matrix Analysis Applications |
Keywords | DocType | Volume |
numerical behavior,adaptive blocksize scheme cure,academic model problem,able method,non-hermitian eigenvalue problems,adaptive block lanczos method,computed lanczos vector,large-scale non-hermitian eigenvalue problem,block version,non-hermitian lanczos algorithm,hermitian lanczos algorithm,eigenvalues,lanczos algorithm | Journal | 20 |
Issue | ISSN | Citations |
4 | 0895-4798 | 23 |
PageRank | References | Authors |
6.11 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaojun Bai | 1 | 661 | 107.69 |
| David Day | 2 | 23 | 6.11 |
| Qiang Ye | 3 | 138 | 18.16 |