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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Vydané v:	SIAM journal on matrix analysis and applications Ročník 39; číslo 4; s. 1701 - 1725
Hlavní autori:	Agullo, Emmanuel, Darve, Eric, Giraud, Luc, Harness, Yuval
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Society for Industrial and Applied Mathematics 01.01.2018
Predmet:	Computer Science Distributed, Parallel, and Cluster Computing Mathematics Numerical Analysis
ISSN:	0895-4798, 1095-7162
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