Journal article

A multilevel Monte Carlo estimator for matrix multiplication

Abstract:: Inspired by recent developments in multilevel Monte Carlo (MLMC) methods and randomized sketching for linear algebra problems, we propose an MLMC estimator for real-time processing of matrix structured random data. Our algorithm is particularly effective in handling high-dimensional inner products and matrix multiplication, and finds applications in computer vision and large-scale supervised learning.

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Wu, Y., & Polydorides, N. (2020). A multilevel Monte Carlo estimator for matrix multiplication. SIAM Journal on Scientific Computing, 42(5), A2731–A2749.

MLA Style

Wu, Y., and N. Polydorides. “A Multilevel Monte Carlo Estimator for Matrix Multiplication.” SIAM Journal on Scientific Computing, vol. 42, no. 5, Society for Industrial & Applied Mathematics, 2020, pp. A2731–A2749.

Chicago Style

Wu, Y, and N Polydorides. 2020. “A Multilevel Monte Carlo Estimator for Matrix Multiplication.” SIAM Journal on Scientific Computing 42 (5): A2731–A2749.
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Files:: Wu et al A multilevel Monte Carlo estimator.pdf

(Preview, Accepted manuscript, 421.6KB, Terms of use)

Publisher copy:: 10.1137/19M125604X

Authors

+ Wu, Y More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Mathematical Institute
Role:: Author

+ Polydorides, N More by this author

Role:: Author

+ Engineering and Physical Sciences Research Council More from this funder

Grant:: EP/S026347/1

Publisher:: Society for Industrial & Applied Mathematics
Journal:: SIAM Journal on Scientific Computing More from this journal
Volume:: 42
Issue:: 5
Pages:: A2731-A2749
Publication date:: 2020-09-15
Acceptance date:: 2020-06-12
DOI:: 10.1137/19M125604X
EISSN:: 1095-7197
ISSN:: 1064-8275

Language:: English
Keywords:: real-time computing

multilevel Monte Carlo

multiplication

FFR

sketching
Pubs id:: 1137935
Local pid:: pubs:1137935
Deposit date:: 2020-11-06

Terms of use

Copyright holder:: Society for Industrial and Applied Mathematics
Copyright date:: 2020
Rights statement:: © 2020, Society for Industrial and Applied Mathematics.
Notes:: This is the accepted manuscript version of the article. The final version is available online from the Society for Industrial and Applied Mathematics at: https://doi.org/10.1137/19M125604X

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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