Minimizing convex quadratics with variable precision Krylov methods

Serge Gratton, Ehouarn Simon, Philippe Toint

Research output: Contribution to journalArticlepeer-review

3 Downloads (Pure)

Abstract

Iterative algorithms for the solution of convex quadratic optimization problems are investigated, which exploit inaccurate matrix-vector products. Theoretical bounds on the performance of a Conjugate Gradients and a Full-Orthormalization methods are derived, the necessary quantities occurring in the theoretical bounds estimated and new practical algorithms derived. Numerical experiments suggest that the new methods have significant potential, including in the steadily more important context of multi-precision computations.
Original languageEnglish
Pages (from-to)e2337
Number of pages26
JournalNumerical Linear Algebra with Applications
Volume28
Issue number1
Publication statusPublished - 1 Oct 2020

Keywords

  • quadratic optimization
  • multi-precision computing
  • linear algebra

Fingerprint Dive into the research topics of 'Minimizing convex quadratics with variable precision Krylov methods'. Together they form a unique fingerprint.

Cite this