Massive memory buys little speed for complete, in-core sparse Cholesky factorizations on some scalar computers

DOI10.1016/0024-3795(91)90279-6zbMath0727.65016OpenAlexW4206729479WikidataQ126352593 ScholiaQ126352593MaRDI QIDQ803714

David M. Gay

Publication date: 1991

Published in: Linear Algebra and its Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0024-3795(91)90279-6

zbMATH Keywords

computational experimentsdirect factorizationsincore sparse Cholesky factorizationssparse, symmetric, positive-definite matrices

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Direct numerical methods for linear systems and matrix inversion (65F05)

Related Items (2)

A survey of direct methods for sparse linear systems ⋮ Recurrent neural networks for LU decomposition and Cholesky factorization

Uses Software

• symrcm

Cites Work

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• An implementation of Karmarkar's algorithm for linear programming
• General Sparse Elimination Requires No Permanent Integer Storage
• The Impact of Hardware Gather/Scatter on Sparse Gaussian Elimination
• A Note on Sparse Factorization in a Paging Environment
• Data Structures and Programming Techniques for the Implementation of Karmarkar's Algorithm

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