# Randomized low-rank approximation of monotone matrix functions

@article{Persson2022RandomizedLA, title={Randomized low-rank approximation of monotone matrix functions}, author={David Persson and Daniel Kressner}, journal={ArXiv}, year={2022}, volume={abs/2209.11023} }

This work is concerned with computing low-rank approximations of a matrix function f ( A ) for a large symmetric positive semi-deﬁnite matrix A , a task that arises in, e.g., statistical learning and inverse problems. The application of popular randomized methods, such as the randomized singular value decomposition or the Nystr¨om approximation, to f ( A ) requires multiplying f ( A ) with a few random vectors. A signiﬁcant dis-advantage of such an approach, matrix-vector products with f ( A…

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