Name
Abstract | ||
|---|---|---|
We introduce a new, quadratically convergent algorithm for finding maximum absolute value entries of tensors represented in the canonical format. The computational complexity of the algorithm is linear in the dimension of the tensor. We show how to use this algorithm to find global maxima of non-convex multivariate functions in separated form. We demonstrate the performance of the new algorithms on several examples. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.jcp.2017.07.012 | Journal of Computational Physics |
Keywords | Field | DocType |
Separated representations,Tensor decompositions,Canonical tensors,Global optimization,Quadratic convergence | Tensor density,Mathematical optimization,Quadratic growth,Tensor,Absolute value,Multivariate statistics,Tensor contraction,Maxima,Mathematics,Computational complexity theory | Journal |
Volume | Issue | ISSN |
348 | C | 0021-9991 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew J. Reynolds | 1 | 4 | 0.76 |
| Gregory Beylkin | 2 | 234 | 30.77 |
| Alireza Doostan | 3 | 188 | 15.57 |