A parallel generator of non‐Hermitian matrices computed from given spectra

Summary Iterative linear algebra methods to solve linear systems and eigenvalue problems with non‐Hermitian matrices are important for both the simulation arising from diverse scientific fields and the applications related to big data, machine learning, and artificial intelligence. The spectral prop...

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Veröffentlicht in:	Concurrency and computation 2020-10, Vol.32 (20), p.n/a
Hauptverfasser:	Wu, Xinzhe, Petiton, Serge G., Lu, Yutong
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial intelligence Cluster analysis Computer Science Computer simulation Distributed, Parallel, and Cluster Computing Eigenvalues Iterative methods Linear algebra linear system and eigenvalue problem Linear systems Machine learning Mathematical analysis matrix generation Matrix methods non‐Hermitian matrix Numerical Analysis parallel computing Solvers Sparse matrices Spectra spectrum Supercomputers
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