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Published July 30, 2019 | Published + Supplemental Material + Submitted
Journal Article Open

Streaming Low-Rank Matrix Approximation with an Application to Scientific Simulation


This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of data matrices that arise from large-scale scientific simulations and data collection. The technical contribution consists in a new algorithm for constructing an accurate low-rank approximation of a matrix from streaming data. This method is accompanied by an a priori analysis that allows the user to set algorithm parameters with confidence and an a posteriori error estimator that allows the user to validate the quality of the reconstructed matrix. In comparison to previous techniques, the new method achieves smaller relative approximation errors and is less sensitive to parameter choices. As concrete applications, the paper outlines how the algorithm can be used to compress a Navier--Stokes simulation and a sea surface temperature dataset.

Additional Information

© 2019 Society for Industrial and Applied Mathematics. Submitted to the journal's Methods and Algorithms for Scientific Computing section July 17, 2018; accepted for publication (in revised form) May 1, 2019; published electronically July 30, 2019. Funding: The work of the first author was partially supported by ONR awards N00014-11-1002, N00014-17-1-214, N00014-17-1-2146, and the Gordon & Betty Moore Foundation. The work of the third author was partially supported by DARPA award FA8750-17-2-0101. The work of the fourth author was supported by the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme under grant agreement 725594 (time-data) and the Swiss National Science Foundation (SNSF) under grant 200021_178865.

Attached Files

Published - 18m1201068.pdf

Submitted - 1902.08651.pdf

Supplemental Material - M120106SupMat1.zip

Supplemental Material - TYUC19-Streaming-Low-Rank-supp-vf.pdf


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August 19, 2023
August 19, 2023