| bibtype |
J -
Journal Article
|
| ARLID |
0618543 |
| utime |
20250423143849.7 |
| mtime |
20250401235959.9 |
| SCOPUS |
105001657523 |
| WOS |
001455423000004 |
| DOI |
10.1109/LSP.2025.3552005 |
| title
(primary) (eng) |
Optimizing the Order of Modes in Tensor Train Decomposition |
| specification |
| page_count |
5 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0253212 |
| ISSN |
1070-9908 |
| title
|
IEEE Signal Processing Letters |
| volume_id |
32 |
| volume |
3 (2025) |
| page_num |
1361-1365 |
| publisher |
| name |
Institute of Electrical and Electronics Engineers |
|
|
| keyword |
Tensors |
| keyword |
Tensor train decomposition |
| keyword |
Matrix product state |
| author
(primary) |
| ARLID |
cav_un_auth*0101212 |
| name1 |
Tichavský |
| name2 |
Petr |
| institution |
UTIA-B |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| full_dept |
Department of Stochastic Informatics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0434606 |
| name1 |
Straka |
| name2 |
O. |
| country |
CZ |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA22-11101S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0435406 |
|
| abstract
(eng) |
The tensor train (TT) is a popular way of representing high-dimensional hyper-rectangular data structures called tensors. It is widely used, for example, in quantum chemistry under the name „matrix product state“. The complexity of the TT model mainly depends on the bond dimensions that connect TT cores, constituting the model. Unlike canonical polyadic decomposition, the TT model complexity may depend on the order of the modes/indices in the data structures or the order of the core tensors in the TT, in general. This paper aims to provide methods for optimizing the order of the modes to reduce the bond dimensions. Since the number of possible orderings of the cores is exponentially high, we propose a greedy algorithm that provides a suboptimal solution. We consider three problem setups, i.e., specifications of the tensor: tensor given by a list of all its elements, tensor described by a TT model with some default order of the modes, and tensor obtained by sampling a multivariate function. |
| result_subspec |
WOS |
| RIV |
IN |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2026 |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0365896 |
| confidential |
S |
| mrcbC91 |
C |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
1.049 |
| mrcbT16-s |
1.271 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q1 |
| arlyear |
2025 |
| mrcbU14 |
105001657523 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001455423000004 WOS |
| mrcbU63 |
cav_un_epca*0253212 IEEE Signal Processing Letters Roč. 32 č. 3 2025 1361 1365 1070-9908 1558-2361 Institute of Electrical and Electronics Engineers |
|