Accurate inverses for computing eigenvalues of extremely ill-conditioned matrices and differential operators

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1 Scopus citations


This paper is concerned with computations of a few smallest eigenvalues (in absolute value) of a large extremely ill-conditioned matrix. It is shown that a few smallest eigenvalues can be accurately computed for a diagonally dominant matrix or a product of diagonally dominant matrices by combining a standard iterative method with the accurate inversion algorithms that have been developed for such matrices. Applications to the finite difference discretization of differential operators are discussed. In particular, a new discretization is derived for the 1-dimensional biharmonic operator that can be written as a product of diagonally dominant matrices. Numerical examples are presented to demonstrate the accuracy achieved by the new algorithms.

Original languageEnglish
Pages (from-to)237-259
Number of pages23
JournalMathematics of Computation
Issue number309
StatePublished - 2018

Bibliographical note

Funding Information:
Received by the editor December 28, 2015 and, in revised form, June 14, 2016 and August 24, 2016. 2010 Mathematics Subject Classification. Primary 65F15, 65F35, 65N06, 65N25. Key words and phrases. Eigenvalue, ill-conditioned matrix, accuracy, Lanczos method, differential eigenvalue problem, biharmonic operator. This research was supported in part by NSF Grants DMS-1317424, DMS-1318633 and DMS-1620082.

Publisher Copyright:
© 2017 American Mathematical Society.


  • Accuracy
  • Biharmonic operator
  • Differential eigenvalue problem
  • Eigenvalue
  • Ill-conditioned matrix
  • Lanczos method

ASJC Scopus subject areas

  • Algebra and Number Theory
  • Computational Mathematics
  • Applied Mathematics


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