Title
TRPL+K: Thick-Restart Preconditioned Lanczos+K Method for Large Symmetric Eigenvalue Problems.
Abstract
The Lanczos method is one of the standard approaches for computing a few eigenpairs of a large, sparse, symmetric matrix. It is typically used with restarting to avoid unbounded growth of memory and computational requirements. Thick-restart Lanczos is a popular restarted variant because of its simplicity and numerical robustness. However, convergence can be slow for highly clustered eigenvalues so more effective restarting techniques and the use of preconditioning is needed. In this paper, we present a thick-restart preconditioned Lanczos method, TRPL+K, that combines the power of locally optimal restarting (+K) and preconditioning techniques with the efficiency of the thick-restart Lanczos method. TRPL+K employs an inner-outer scheme where the inner loop applies Lanczos on a preconditioned operator while the outer loop augments the resulting Lanczos subspace with certain vectors from the previous restart cycle to obtain eigenvector approximations with which it thick restarts the outer subspace. We first identify the differences from various relevant methods in the literature. Then, based on an optimization perspective, we show an asymptotic global quasi optimality of a simplified TRPL+K method compared to an unrestarted global optimal method. Finally, we present extensive experiments showing that TRPL+K either outperforms or matches other state-of-the-art eigenmethods in both matrix-vector multiplications and computational time.
Year
DOI
Venue
2017
10.1137/17M1157568
SIAM JOURNAL ON SCIENTIFIC COMPUTING
Keywords
Field
DocType
symmetric eigenvalue problems,thick-restart,preconditioned Lanczos,global quasi optimality
Inner loop,Convergence (routing),Applied mathematics,Discrete mathematics,Lanczos resampling,Subspace topology,Symmetric matrix,Robustness (computer science),Operator (computer programming),Mathematics,Eigenvalues and eigenvectors
Journal
Volume
Issue
ISSN
41
2
1064-8275
Citations 
PageRank 
References 
0
0.34
14
Authors
3
Name
Order
Citations
PageRank
Lingfei Wu111632.05
Fei Xue26413.70
Andreas Stathopoulos3283.42