Matches in SemOpenAlex for { <https://semopenalex.org/work/W3206999153> ?p ?o ?g. }
- W3206999153 abstract "This work explores how randomization can be exploited to deliver sophisticatedalgorithms with provable bounds for: (i) The approximation of matrix functions, suchas the log-determinant and the Von-Neumann entropy; and (ii) The low-rank approximationof matrices. Our algorithms are inspired by recent advances in RandomizedNumerical Linear Algebra (RandNLA), an interdisciplinary research area that exploitsrandomization as a computational resource to develop improved algorithms forlarge-scale linear algebra problems. The main goal of this work is to encourage thepractical use of RandNLA approaches to solve Big Data bottlenecks at industriallevel. Our extensive evaluation tests are complemented by a thorough theoreticalanalysis that proves the accuracy of the proposed algorithms and highlights theirscalability as the volume of data increases. Finally, the low computational time andmemory consumption, combined with simple implementation schemes that can easilybe extended in parallel and distributed environments, render our algorithms suitablefor use in the development of highly efficient real-world software." @default.
- W3206999153 created "2021-10-25" @default.
- W3206999153 creator A5044158933 @default.
- W3206999153 date "2020-07-28" @default.
- W3206999153 modified "2023-09-22" @default.
- W3206999153 title "RANDOMIZED NUMERICAL LINEAR ALGEBRA APPROACHES FOR APPROXIMATING MATRIX FUNCTIONS" @default.
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- W3206999153 doi "https://doi.org/10.25394/pgs.12730334.v1" @default.
- W3206999153 hasPublicationYear "2020" @default.