Computing Low-Rank Approximations of Large-Scale Matrices with the Tensor Network Randomized SVD
We propose a new algorithm for the computation of a singular value decomposition (SVD) low-rank approximation of a matrix in the matrix product operator (MPO) format, also called the tensor train matrix format. Our tensor network randomized SVD (TNrSVD) algorithm is an MPO implementation of the rand...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Society for Industrial & Applied Mathematics (SIAM),
2019-07-09T19:10:20Z.
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| Subjects: | |
| Online Access: | Get fulltext |