On the block Lanczos and block Golub-Kahan reduction methods applied to discrete ill-posed problems

Abdulaziz Alqahtani, Silvia Gazzola, Lothar Reichel, Giuseppe Rodriguez

Research output: Contribution to journalArticlepeer-review

Abstract



The reduction of a large‐scale symmetric linear discrete ill‐posed problem with multiple right‐hand sides to a smaller problem with a symmetric block tridiagonal matrix can easily be carried out by the application of a small number of steps of the symmetric block Lanczos method. We show that the subdiagonal blocks of the reduced problem converge to zero fairly rapidly with increasing block number. This quick convergence indicates that there is little advantage in expressing the solutions of discrete ill‐posed problems in terms of eigenvectors of the coefficient matrix when compared with using a basis of block Lanczos vectors, which are simpler and cheaper to compute. Similarly, for nonsymmetric linear discrete ill‐posed problems with multiple right‐hand sides, we show that the solution subspace defined by a few steps of the block Golub–Kahan bidiagonalization method usually can be applied instead of the solution subspace determined by the singular value decomposition of the coefficient matrix without significant, if any, reduction of the quality of the computed solution.
Original languageEnglish
Article numbere2376
JournalNumerical Linear Algebra with Applications
Volume28
Issue number5
Early online date27 Mar 2021
DOIs
Publication statusPublished - 31 Oct 2021

Keywords

  • Golub–Kahan block bidiagonalization
  • Tikhonov regularization
  • large-scale discrete ill-posed problem
  • symmetric Lanczos block tridiagonalization

ASJC Scopus subject areas

  • Algebra and Number Theory
  • Applied Mathematics

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