Work

Minimizing convex quadratic with variable precision conjugate gradients

Year: 2018

Type: preprint

Abstract: We investigate the method of conjugate gradients, exploiting inaccurate matrix-vector products, for the solution of convex quadratic optimization problems. Theoretical performance bounds are derived, and the necessary quantities occurring in the theoretical bounds estimated, leading to a practical algorithm. Numerical experiments suggest that this approach has significant potential, including in the steadily more important context of multi-precision computations

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1807.07476

Authors: Gratton, S., Simon, E., Titley-Peloquin, D., Toint, Ph. L.

Institutions: Institut National Polytechnique de Toulouse, Institut de Recherche en Informatique de Toulouse, Université de Toulouse, McGill University, University of Namur

Language: English

Topic: Matrix Theory and Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Agence Nationale de la Recherche, Centre International de Mathématiques et Informatique de Toulouse

Awards: G4414853111, G4866930200, Université Fédérale de Toulouse, G5261993341, G6942917955, Centre International de Mathématiques et d'Informatique (de Toulouse), G8938184468

OpenAlex ID: W3087186941

Cites: 30

Cited by:

Related to: 20