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.

翻译：大型矩阵配对部分通用单值分解(GSVD)的计算方法,可以通过基于扩展子空间的迭代方法,特别是Krylov子空间的迭代方法进行。我们考虑使用联合Lanczos 排列法,并分析在其他线性代数问题中成功应用厚重的重新启动技术的可行性。数字实验显示了拟议方法的有效性。我们还将新方法与替代方法进行比较,通过等量的精度和计算性能问题,同时考虑精度和计算性能。分析是利用SLEPc图书馆的平行实施完成的。