Name
Abstract | ||
|---|---|---|
The Hadamard product features prominently in tensor-based algorithms in scientific computing and data analysis. Due to its tendency to significantly increase ranks, the Hadamard product can represent a major computational obstacle in algorithms based on low-rank tensor representations. It is therefore of interest to develop recompression techniques that mitigate the effects of this rank increase. In this work, we investigate such techniques for the case of the Tucker format, which is well suited for tensors of low order and small to moderate multilinear ranks. Fast algorithms are attained by combining iterative methods, such as the Lanczos method and randomized algorithms, with fast matrix-vector products that exploit the structure of Hadamard products. The resulting complexity reduction is particularly relevant for tensors featuring large mode sizes I and small to moderate multilinear ranks R. To implement our algorithms, we have created a new Julia library for tensors in Tucker format. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1137/16M1093896 | SIAM JOURNAL ON SCIENTIFIC COMPUTING |
Keywords | Field | DocType |
tensors,Tucker format,HOSVD,Hadamard product | Mathematical optimization,Tensor,Iterative method,Hadamard product,Low-rank approximation,Tucker decomposition,Higher-order singular value decomposition,Hadamard transform,Multilinear map,Mathematics | Journal |
Volume | Issue | ISSN |
39 | 5 | 1064-8275 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Kressner | 1 | 449 | 48.01 |
| Lana Perisa | 2 | 0 | 0.34 |