Filter factors of truncated TLS regularization with multiple observations

Motivated by the recently popular probabilistic methods for low-rank approximations and randomized algorithms for the least squares problems, we develop randomized algorithms for the total least squares problem with a single right-hand side. We present the Nyström method for the medium-sized problems. For the large-scale and ill-conditioned cases, we introduce the randomized truncated total least squares with the known or estimated rank as the regularization parameter. We analyze the accuracy of the algorithm randomized truncated total least squares and perform numerical experiments to demonstrate the efficiency of our randomized algorithms. The randomized algorithms can greatly reduce the computational time and still maintain good accuracy with very high probability.

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Randomized algorithms for total least squares problems

Xie

Xiang

Wei

2018

Numerical Linear Algebra App

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Filter factors of truncated TLS regularization with multiple observations

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Cited by 1 publication

References 21 publications

Randomized algorithms for total least squares problems

Randomized algorithms for total least squares problems

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