ScholarLens Back to search
2006•Unpublished venueRequires access

Study of Krylov subspace algorithm

Jiang Shen-ming

Open publisher page 0 citations

Abstract

Krylov subspace methods are considered currently to be among the most important iterative techniques available for solving large-scale linear systems.These techniques are based on projection process,both orthogonal and oblique,onto Krylov subspaces.The efficient preconditioned methods will accelerate the convergence of the algorithm.This paper introduces how to solve large scale linear problems by GMRES based on LU factorizatinos.

About this research paper

What this paper is about

Krylov subspace methods are considered currently to be among the most important iterative techniques available for solving large-scale linear systems.These techniques are based on projection process,both orthogonal and oblique,onto Krylov subspaces.The efficient preconditioned methods will accelerate the convergence of the algorithm.This paper introduces how to solve large scale linear problems by GMRES based on LU factorizatinos.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Krylov subspace methods are considered currently to be among the most important iterative techniques available for solving large-scale linear systems.These techniques are based on projection process,both orthogonal and oblique,onto Krylov subspaces.The efficient preconditioned methods will accelerate the convergence of the algorithm.This paper introduces how to solve large scale linear problems by GMRES based on LU factorizatinos.

Key concepts: Krylov subspace, Generalized minimal residual method, Linear subspace, Algorithm, Linear system, Oblique projection, Computer science, Convergence (economics)

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
Study of Krylov subspace algorithm — Research Paper | ScholarLens