A fast randomized eigensolver with structured LDL factorization update
Publication in refereed journal

香港中文大學研究人員
替代計量分析
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其它資訊
摘要In this paper, we propose a structured bisection method with adaptive randomized sampling for finding selected or all of the eigenvalues of certain real symmetric matrices A. For A with a low-rank property, we construct a hierarchically semiseparable (HSS) approximation and show how to quickly evaluate and update its inertia in the bisection method. Unlike some existing randomized HSS constructions, the methods here do not require the knowledge of the off-diagonal (numerical) ranks in advance. Moreover, for A with a weak rank property or slowly decaying off-diagonal singular values, we show an idea for aggressive low-rank inertia evaluation, which means that a compact HSS approximation can preserve the inertia for certain shifts. This is analytically justified for a special case, and numerically shown for more general ones. A generalized LDL factorization of the HSS approximation is then designed for the fast evaluation of the inertia. A significant advantage over standard LDL factorizations is that the HSS LDL factorization (and thus the inertia) of A - sI can be quickly updated with multiple shifts s in bisection. The factorization with each new shift can reuse about 60% of the work. As an important application, the structured eigensolver can be applied to symmetric Toeplitz matrices, and the cost to find one eigenvalue is nearly linear in the order of the matrix. The numerical examples demonstrate the efficiency and the accuracy of our methods, especially the benefit of low-rank inertia evaluations. The ideas and methods can be potentially adapted to other HSS computations where shifts are involved and to more problems without a significant low-rank property.
著者Xi Y., Xia J., Chan R.
期刊名稱SIAM Journal on Matrix Analysis and Applications
出版年份2014
月份1
日期1
卷號35
期次3
出版社Society for Industrial and Applied Mathematics
出版地United States
頁次974 - 996
國際標準期刊號0895-4798
語言英式英語
關鍵詞Adaptive randomized sampling, Aggressive low-rank inertia evaluation, Eigensolver, HSS matrix, Inertia/LDL update for varying shifts, Matrix-free HSS construction

上次更新時間 2020-17-10 於 01:11