Squeezing the most out of eigenvalue solvers on high-performance computers

DOI10.1016/0024-3795(86)90164-3zbMath0587.65027OpenAlexW2002991695MaRDI QIDQ1072342

Linda Kaufman, Jack J. Dongarra, Sven J. Hammarling

Publication date: 1986

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

Full work available at URL: https://doi.org/10.1016/0024-3795(86)90164-3

zbMATH Keywords

eigenvalueseigenvectorshigh-performance vector computers

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15)

Related Items

Linear algebra on high performance computers, An overview of parallel algorithms for the singular value and symmetric eigenvalue problems, The arithmetic mean preconditioner for multivector computers, Solving emission tomography problems on vector machines, Block reduction of matrices to condensed forms for eigenvalue computations, Squeezing the most out of eigenvalue solvers on high-performance computers

Uses Software

• EISPACK

Cites Work

• Squeezing the most out of eigenvalue solvers on high-performance computers
• Matrix eigensystem routines - EISPACK guide. 2nd ed
• Matrix eigensystem routines. EISPACK guide extension
• Banded Eigenvalue Solvers on Vector Machines
• An Improved Algorithm for Computing the Singular Value Decomposition
• Basic Linear Algebra Subprograms for Fortran Usage
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