Gimbutas, Z.
and Gillman, A.
(2026),
librla: A library of randomized linear algebra routines, (*TBD - To Be Determined*) (Accessed July 28, 2026)
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librla: A library of randomized linear algebra routines
Published
Author(s)
, Adrianna Gillman
Abstract
Randomized linear algebra algorithms have become a vital tool for a variety of areas including fast direct solvers, reduced order modeling, and data science. Additionally, randomized linear algebra provides useful tools for solving total least squares problems, rank deficient least squares problem, doing matrix approximation, and skeletonizing (i.e. subset selection) a matrix. librla provides low rank QR factorizations, SVDs and interpolatory decompositions written natively in Python, Julia and Matlab. The algorithms randomly sample the range of the matrix or operator in a similar manner to (Halko et al., 2011). A key feature of this package is that it is designed to exploit Level 3 BLAS operators as much as possible. librla is designed for small to mid-range sized matrices (i.e. up to roughly 10,000 in size depending on computing resources).Citation
(*TBD - To Be Determined*)
Pub Type
Journals
Keywords
Randomized methods, QR factorization, singular value decomposition, interpolatory decomposition, Python, Matlab, Octave, Julia
Citation
Issues
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Created July 27, 2026