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.


Published in:
SIAM Journal on Scientific Computing, 41, 4, A2430-A2463
Year:
Jan 01 2019
ISSN:
1064-8275
1095-7197
Keywords:
Laboratories:


Note: The status of this file is: Anyone


 Record created 2019-09-19, last modified 2020-04-20

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