论文标题
GSVD的厚持续关节兰斯佐斯双节化
Thick-restarted joint Lanczos bidiagonalization for the GSVD
论文作者
论文摘要
可以通过基于扩展子空间（尤其是Krylov子空间）的迭代方法来了解大规模基质对的部分广义奇异值分解（GSVD）的计算。我们考虑了联合兰氏双节性化方法，并分析适应在其他线性代数问题背景下成功使用的厚重新启动技术的可行性。数值实验说明了提出的方法的有效性。我们还考虑到准确性和计算性能，将新方法与替代解决方案进行了比较。分析是使用SLEPC库中的并行实现进行的。
The computation of the partial generalized singular value decomposition (GSVD) of large-scale matrix pairs can be approached by means of iterative methods based on expanding subspaces, particularly Krylov subspaces. We consider the joint Lanczos bidiagonalization method, and analyze the feasibility of adapting the thick restart technique that is being used successfully in the context of other linear algebra problems. Numerical experiments illustrate the effectiveness of the proposed method. We also compare the new method with an alternative solution via equivalent eigenvalue problems, considering accuracy as well as computational performance. The analysis is done using a parallel implementation in the SLEPc library.
