Parallel iterative methods for sparse linear systems

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Scopus citations

Abstract

This paper presents an overview of parallel algorithms and their implementations forsolving large sparse linear systems which arise in scientific and engineering applications. Preconditioners constitute the most important ingredient in solving such systems. As will be seen, the most common preconditioners used for sparse linear systems adapt domain decomposition concepts to the more general framework of "distributed sparse linear systems". Variants of Schwarz procedures and Schur complement techniques will be discussed. We will also report on our own experience in the parallel implementation of a fairly complex simulation of solid-liquid flows.

Original languageEnglish (US)
Title of host publicationStudies in Computational Mathematics
PublisherElsevier
Pages423-440
Number of pages18
EditionC
DOIs
StatePublished - 2001

Publication series

NameStudies in Computational Mathematics
NumberC
Volume8
ISSN (Print)1570-579X

Fingerprint Dive into the research topics of 'Parallel iterative methods for sparse linear systems'. Together they form a unique fingerprint.

Cite this