The generalized eigenvalue problem Ax = hBx with a non-symmetric matrix A is solved by means of inverse vector iteration. The algorithm makes use of the band structure of the matrices, thus allowing quite large dimensions (d 5 3742). In the application all complex eigenvalues for the resistive Alfve
Bounding the spectrum of large Hermitian matrices
β Scribed by Yunkai Zhou; Ren-Cang Li
- Publisher
- Elsevier Science
- Year
- 2011
- Tongue
- English
- Weight
- 398 KB
- Volume
- 435
- Category
- Article
- ISSN
- 0024-3795
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β¦ Synopsis
Estimating upper bounds of the spectrum of large Hermitian matrices has long been a problem with both theoretical and practical significance. Algorithms that can compute tight upper bounds with minimum computational cost will have applications in a variety of areas. We present a practical algorithm that exploits kstep Lanczos iteration with a safeguard step. The k is generally very small, say 5-8, regardless of the large dimension of the matrices. This makes the Lanczos iteration economical. The safeguard step can be realized with marginal cost by utilizing the theoretical bounds developed in this paper. The bounds establish the theoretical validity of a previous bound estimator that has been successfully used in various applications. Moreover, we improve the bound estimator which can now provide tighter upper bounds with negligible additional cost.
π SIMILAR VOLUMES
We give a bound for the perturbations of invariant subspaces of graded indefinite Hermitian matrix H = D \* AD which is perturbed into H + Ξ΄H = D \* (A + Ξ΄A)D. Such relative perturbations include an important case where H is given with an element-wise relative error. Application of our bounds requir