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: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Society for Industrial & Applied Mathematics (SIAM)
2019
|
| Online Access: | https://hdl.handle.net/1721.1/121552 |