Name
Title | ||
|---|---|---|
Two Harmonic Jacobi–Davidson Methods for Computing a Partial Generalized Singular Value Decomposition of a Large Matrix Pair |
Abstract | ||
|---|---|---|
Two harmonic extraction based Jacobi–Davidson (JD) type algorithms are proposed to compute a partial generalized singular value decomposition (GSVD) of a large regular matrix pair. They are called cross product-free (CPF) and inverse-free (IF) harmonic JDGSVD algorithms, abbreviated as CPF-HJDGSVD and IF-HJDGSVD, respectively. Compared with the standard extraction based JDGSVD algorithm, the harmonic extraction based algorithms converge more regularly and suit better for computing GSVD components corresponding to interior generalized singular values. Thick-restart CPF-HJDGSVD and IF-HJDGSVD algorithms with some deflation and purgation techniques are developed to compute more than one GSVD components. Numerical experiments confirm the superiority of CPF-HJDGSVD and IF-HJDGSVD to the standard extraction based JDGSVD algorithm. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s10915-022-01993-7 | Journal of Scientific Computing |
Keywords | DocType | Volume |
Generalized singular value decomposition, Generalized singular value, Generalized singular vector, Standard extraction, Harmonic extraction, Jacobi–Davidson type method, 65F15, 15A18, 65F10 | Journal | 93 |
Issue | ISSN | Citations |
2 | 0885-7474 | 0 |
PageRank | References | Authors |
0.34 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinzhi Huang | 1 | 0 | 0.34 |
| Zhongxiao Jia | 2 | 121 | 18.57 |