Minimizing convex quadratics with variable precision Krylov methods

Serge Gratton, Ehouarn Simon, Philippe Toint

Résultats de recherche: Papier de travailArticle de travail

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Résumé

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.
langue originaleAnglais
ÉditeurArxiv
Nombre de pages26
Volume1807.07476
étatSoumis - 2018

Empreinte digitale

Krylov Methods
Quadratic Optimization
Cross product
Matrix Product
Conjugate Gradient
Inaccurate
Convex Optimization
Iterative Algorithm
Numerical Experiment
Optimization Problem
Necessary
Experiments
Context

Citer ceci

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Minimizing convex quadratics with variable precision Krylov methods. / Gratton, Serge; Simon, Ehouarn; Toint, Philippe.

Arxiv, 2018.

Résultats de recherche: Papier de travailArticle de travail

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