RSVDPACK

RSVDPACK: subroutines for computing partial singular value decompositions via randomized sampling on single core, multi core, and GPU architectures. This document describes an implementation in C of a set of randomized algorithms for computing partial Singular Value Decompositions (SVDs). The techniques largely follow the prescriptions in the article ”Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions,” N. Halko, P.G. Martinsson, J. Tropp, SIAM Review, 53(2), 2011, pp. 217-288, but with some modifications to improve performance. The codes implement a number of low rank SVD computing routines for three different sets of hardware: (1) single core CPU, (2) multi core CPU, and (3) massively multicore GPU.

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References in zbMATH (referenced in 6 articles )

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  1. Alla, Alessandro; Kutz, J. Nathan: Randomized model order reduction (2019)
  2. Erichson, N. Benjamin; Mathelin, Lionel; Kutz, J. Nathan; Brunton, Steven L.: Randomized dynamic mode decomposition (2019)
  3. N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz: Randomized Matrix Decompositions Using R (2019) not zbMATH
  4. Yu, Wenjian; Gu, Yu; Li, Yaohang: Efficient randomized algorithms for the fixed-precision low-rank matrix approximation (2018)
  5. Voronin, Sergey; Martinsson, Per-Gunnar: Efficient algorithms for CUR and interpolative matrix decompositions (2017)
  6. Voronin, Sergey; Mikesell, Dylan; Nolet, Guust: Compression approaches for the regularized solutions of linear systems from large-scale inverse problems (2015)