Fast and accurate randomized algorithms for linear systems and eigenvalue problems
This paper develops a class of algorithms for general linear systems and eigenvalue problems. These algorithms apply fast randomized dimension reduction (``sketching"") to accelerate standard subspace projection methods, such as GMRES and Rayleigh--Ritz. This modification makes it possible...
| 主要な著者: | , |
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| フォーマット: | Journal article |
| 言語: | English |
| 出版事項: |
Society for Industrial and Applied Mathematics
2024
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| 要約: | This paper develops a class of algorithms for general linear systems and eigenvalue problems. These algorithms apply fast randomized dimension reduction (``sketching"") to accelerate standard subspace projection methods, such as GMRES and Rayleigh--Ritz. This modification makes it possible to incorporate nontraditional bases for the approximation subspace that are easier to construct. When the basis is numerically full rank, the new algorithms have accuracy similar to classic methods but run faster and may use less storage. For model problems, numerical experiments show large advantages over the optimized MATLAB routines, including a 70\times speedup over gmres and a 10\times speedup over eigs. |
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