Work

Computing Singular Values of Large Matrices with an Inverse-Free Preconditioned Krylov Subspace Method

Year: 2014

Type: article

Abstract: We present an efficient algorithm for computing a few extreme singular values of a large sparse m×n matrix C. Our algorithm is based on reformulating the singular value problem as an eigenvalue problem for CTC. To address the clustering of the singular values, we develop an inverse-free preconditioned Krylov subspace method to accelerate convergence. We consider preconditioning that is based on robust incomplete factorizations, and we discuss various implementation issues. Extensive numerical tests are presented to demonstrate efficiency and robustness of the new algorithm.

Source: UKnowledge (University of Kentucky)

Authors: Liang Qiao, Qiang Ye

Language: English

Topic: Matrix Theory and Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Awards: Accurate and Efficient Algorithms for Computing Exponentials of Large Matrices with Applications, Collaborative Research: CDS&E-MSS: Robust Algorithms for Interpolation and Extrapolation in Manifold Learning

OpenAlex ID: W2550348766

FWCI: 2.627

Cites: 43

Cited by: 16

Related to: 10