Cluster robustness of preconditioned gradient subspace iteration eigensolvers
Cluster robustness of preconditioned gradient subspace iteration eigensolvers
复制标题
预条件梯度子空间迭代特征求解器的聚类鲁棒性
DOI：
10.1016/j.laa.2005.06.039
复制
发表时间：
2006
影响因子：
1.1
通讯作者：
E. Ovtchinnikov
中科院分区：
文献类型：
--
作者：
E. Ovtchinnikov
本文给出了求解部分广义对称特征值问题的一类迭代方法的收敛性估计，该方法通过结合Rayleigh-Ritz和预条件最速下降/上升方法生成包含特征向量逼近的子空间序列.本文采用了一种新的方法，研究组的特征值的收敛性，而不是个别的，以获得新的收敛估计这类方法是集群的强大，即不涉及计算的特征值之间的距离。
The paper presents convergence estimates for a class of iterative methods for solving partial generalized symmetric eigenvalue problems whereby a sequence of subspaces containing approximations to eigenvectors is generated by combining the Rayleigh–Ritz and the preconditioned steepest descent/ascent methods. The paper uses a novel approach of studying the convergence of groups of eigenvalues, rather than individual ones, to obtain new convergence estimates for this class of methods that are cluster robust, i.e. do not involve distances between computed eigenvalues.