A blocked randomized low-rank approximation algorithm reveals numerical rank adaptively to a spectral threshold, with new bounds on deflation accuracy.
As stated in ( 2.1)-(2.4), the quality of column-orthonormal matrix Q plays crucial roles in the accuracy of the algorithm
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Efficient adaptive randomized algorithms for fixed-threshold low-rank matrix approximation
A blocked randomized low-rank approximation algorithm reveals numerical rank adaptively to a spectral threshold, with new bounds on deflation accuracy.