Randomized LU decomposition

Gil Shabat, Yaniv Shmueli, Yariv Aizenbud, Amir Averbuch*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Randomized algorithms play a central role in low rank approximations of large matrices. In this paper, the scheme of the randomized SVD is extended to a randomized LU algorithm. Several error bounds are introduced, that are based on recent results from random matrix theory related to subgaussian matrices. The bounds also improve the existing bounds of already known randomized SVD algorithm. The algorithm is fully parallelized and thus can utilize efficiently GPUs without any CPU–GPU data transfer. Numerical examples, which illustrate the performance of the algorithm and compare it to other decomposition methods, are presented.

Original languageEnglish
Pages (from-to)246-272
Number of pages27
JournalApplied and Computational Harmonic Analysis
Issue number2
StatePublished - 1 Mar 2018


  • LU decomposition
  • Matrix factorizations
  • Random matrices
  • Randomized algorithms


Dive into the research topics of 'Randomized LU decomposition'. Together they form a unique fingerprint.

Cite this