Minimizing convex quadratics with variable precision Krylov methods

Serge Gratton; Ehouarn Simon; Philippe Toint

Minimizing convex quadratics with variable precision Krylov methods

Serge Gratton, Ehouarn Simon, Philippe Toint

Namur Institute for Complex Systems

Résultats de recherche: Contribution à un journal/une revue › Article › Revue par des pairs

3 Téléchargements (Pure)

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 originale	Anglais
Pages (de - à)	e2337
Nombre de pages	26
journal	Numerical Linear Algebra with Applications
Volume	28
Numéro de publication	1
Etat de la publication	Publié - 1 oct. 2020

Accès au document

2020_TointP_articlemanuscrit soumis, 417 KBLicense: Non spécifié

Empreinte digitale Examiner les sujets de recherche de « Minimizing convex quadratics with variable precision Krylov methods ». Ensemble, ils forment une empreinte digitale unique.

3 Discours invité
1 Visite à une institution académique externe

ENSEEIHT-IRIT
Philippe Toint (Chercheur visiteur)
4 nov. 2019 → 8 nov. 2019
Activité: Types de Visite d'une organisation externe › Visite à une institution académique externe
Minimizing convex quadratics with variable precision Krylov methods
Philippe Toint (Orateur)
28 nov. 2019
Activité: Types de discours ou de présentation › Discours invité
Minimizing convex quadratics with variable precision Krylov methods
Philippe Toint (Orateur)
10 oct. 2019
Activité: Types de discours ou de présentation › Discours invité

Contient cette citation

@article{9229d398d2974aafac2c6f7992a14fcb,

title = "Minimizing convex quadratics with variable precision Krylov methods",

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.",

keywords = "quadratic optimization, multi-precision computing, linear algebra",

author = "Serge Gratton and Ehouarn Simon and Philippe Toint",

year = "2020",

month = oct,

day = "1",

language = "English",

volume = "28",

pages = "e2337",

journal = "Numerical Linear Algebra with Applications",

issn = "1070-5325",

publisher = "John Wiley and Sons Ltd",

number = "1",

}

TY - JOUR

T1 - Minimizing convex quadratics with variable precision Krylov methods

AU - Gratton, Serge

AU - Simon, Ehouarn

AU - Toint, Philippe

PY - 2020/10/1

Y1 - 2020/10/1

N2 - 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.

AB - 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.

KW - quadratic optimization

KW - multi-precision computing

KW - linear algebra

M3 - Article

VL - 28

SP - e2337

JO - Numerical Linear Algebra with Applications

JF - Numerical Linear Algebra with Applications

SN - 1070-5325

IS - 1

ER -

Minimizing convex quadratics with variable precision Krylov methods

Résumé

Accès au document

ENSEEIHT-IRIT