Minimizing convex quadratics with variable precision Krylov methods

Serge Gratton, Ehouarn Simon, Philippe Toint

Research output: Working paper

12 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
PublisherArxiv
Number of pages26
Volume1807.07476
Publication statusSubmitted - 2018

Keywords

  • quadratic optimization
  • multi-precision computing
  • linear algebra

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  • Activities

    • 3 Invited talk
    • 1 Visiting an external academic institution

    ENSEEIHT-IRIT

    Philippe Toint (Visiting researcher)

    4 Nov 20198 Nov 2019

    Activity: Visiting an external institution typesVisiting an external academic institution

    Minimizing convex quadratics with variable precision Krylov methods

    Philippe Toint (Speaker)

    28 Nov 2019

    Activity: Talk or presentation typesInvited talk

    Minimizing convex quadratics with variable precision Krylov methods

    Philippe Toint (Speaker)

    10 Oct 2019

    Activity: Talk or presentation typesInvited talk

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