Projection acceleration of Krylov solvers | Zendy

Vuik C. | Zendy; Tang J.M. | Zendy; Nabben R. | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

Projection acceleration of Krylov solvers

Author(s) -

Vuik C.,

Tang J.M.,

Nabben R.

Publication year - 2007

Publication title -

pamm

Language(s) - English

Resource type - Journals

ISSN - 1617-7061

DOI - 10.1002/pamm.200700949

Subject(s) - convergence (economics) , eigenvalues and eigenvectors , projection (relational algebra) , acceleration , mathematics , projection method , computer science , mathematical optimization , algorithm , dykstra's projection algorithm , physics , classical mechanics , quantum mechanics , economics , economic growth

In many applications it appears that the initial convergence of preconditioned Krylov solvers is slow. The reason for this is that a number of small eigenvalues are present. After these bad eigenvector components are approximated, the fast superlinear convergence sets in. A way to have fast convergence from the start is to remove these components by a projection. In this paper we give a comparison of some of these projection operators. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research