Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: New methods for computing eigenvectors of symmetric block tridiagonal matrices based on twisted block factorizations are explored. The relation of the block where two twisted factorizations meet to an eigenvector of the block tridiagonal matrix is revied. Based on this, several new algorithmic strategies for computing the eigenvector efficiently are motivated and designed. The underlying idea is to determine a good starting vector for an inverse iteration process from the twisted block factorizations such that a good eigenvector approximation can be computed with a single step of inverse iteration.An implementation of the new algorithms is presented and experimental data for runtime behaviour and numerical accuracy based on a wide range of test cases are summarized. Compared with competing state-of-the-art tridiagonalization-based methods, the algorithms proposed here show strong reductions in runtime, especially for very large matrices and/or small bandwidths. The residuals of the computed eigenvectors are in general comparable with state-of-the-art methods. In some cases, especially for strongly clustered eigenvalues, a loss in orthogonality of some eigenvectors is observed. This is not surprising, and future work will focus on investigating ways for improving these cases.
['Gerhard König', 'Michael Moldaschl', 'Wilfried N. Gansterer']
I am writing a research paper on a idea Computing eigenvectors of block tridiagonal matrices based on twisted block factorizations
[{"from": "human", "value": "I am writing a research paper on a idea Computing eigenvectors of block tridiagonal matrices based on twisted block factorizations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: New methods for computing eigenvectors of symmetric block tridiagonal matrices based on twisted block factorizations are explored. The relation of the block where two twisted factorizations meet to an eigenvector of the block tridiagonal matrix is revied. Based on this, several new algorithmic strategies for computing the eigenvector efficiently are motivated and designed. The underlying idea is to determine a good starting vector for an inverse iteration process from the twisted block factorizations such that a good eigenvector approximation can be computed with a single step of inverse iteration.An implementation of the new algorithms is presented and experimental data for runtime behaviour and numerical accuracy based on a wide range of test cases are summarized. Compared with competing state-of-the-art tridiagonalization-based methods, the algorithms proposed here show strong reductions in runtime, especially for very large matrices and/or small bandwidths. The residuals of the computed eigenvectors are in general comparable with state-of-the-art methods. In some cases, especially for strongly clustered eigenvalues, a loss in orthogonality of some eigenvectors is observed. This is not surprising, and future work will focus on investigating ways for improving these cases."}]