Low-Rank Factorizations in Data Sparse Hierarchical Algorithms for Preconditioning Symmetric Positive Definite Matrices

We consider the problem of choosing low-rank factorizations in data sparse matrix approximations for preconditioning large-scale symmetric positive definite (SPD) matrices. These approximations are memory-efficient schemes that rely on hierarchical matrix partitioning and compression of certain sub-...

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Bibliographic Details

Published in:	SIAM journal on matrix analysis and applications Vol. 39; no. 4; pp. 1701 - 1725
Main Authors:	Agullo, Emmanuel, Darve, Eric, Giraud, Luc, Harness, Yuval
Format:	Journal Article
Language:	English
Published:	Society for Industrial and Applied Mathematics 01.01.2018
Subjects:	Computer Science Distributed, Parallel, and Cluster Computing Mathematics Numerical Analysis
ISSN:	0895-4798, 1095-7162
Online Access:	Get full text
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