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Bilateral sampling randomized singular value decomposition.

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Li,  H.
Research Group of Statistical Inverse-Problems in Biophysics, MPI for Biophysical Chemistry, Max Planck Society;

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Citation

Jiang, H., Du, P., Sun, T., Li, H., Cheng, L., & Yang, C. (2016). Bilateral sampling randomized singular value decomposition. In H. Shen, Y. Sang, & H. Tian (Eds.), 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) 2016 (pp. 57-62).


Cite as: https://hdl.handle.net/11858/00-001M-0000-002D-9EE4-B
Abstract
Designing fast singular value decomposition (SVD) is significantly interesting in applications. The random direct SVD (RSVD) has provided a fast scheme to compute the well approximate SVD by unilateral randomized sampling. In this paper, we present an efficient random algorithm in a bilateral sampling way. We also prove that the proposed algorithms can be bounded well and have less computational complexity compared to RSVD when the objective matrix is approximately square. Numerical experiments on graph Laplacian and Hilbert matrix demonstrate the efficiency and stability of the proposed methods.