0574036 20240402214222.420230801235959.9 5 s. P cav_un_epca*0574035978-1-6654-5244-1557-561PiscatawayIEEE2023 multilinear models tensor train Rosenbrock function cav_un_auth*0101212 Tichavský Petr UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0274170 Phan A. H. JP http://library.utia.cas.cz/separaty/2023/SI/tichavsky-0574036.pdf 374 kB GA22-11101S GA ČR CZ cav_un_auth*0435406 Tensor Chain (TC) decomposition represents a given tensor as a chain (circle) of order-3 tensors (wagons) connected through tensor contractions. In this paper, we show the link between the TC decomposition and a structured Tucker decompositions, and propose a variant of the Krylov-Levenberg-Marquardt optimization, tailored for this problem. Many extensions can be considered, here we only mention decomposition of tensor with missing entries, which enables the tensor completion. Performance of the proposed algorithm is demonstrated on tensor decomposition of the sampled Rosenbrock function. It can be better modeled both as TC and canonical polyadic (CP) decomposition, but with TC, the reconstruction is possible with a lower number of function values. cav_un_auth*0452745 IEEE Statistical Signal Processing Workshop /22./ 20230702 20230705 Hanoi VN BB 20000 20200 20201 2024 2 PO RVO:67985556 https://hdl.handle.net/11104/0344729 S 2023 SCOPUS PUBMED WOS 374 kB cav_un_epca*0574035 Proceedings of the 22nd IEEE Statistical Signal Processing Workshop 978-1-6654-5244-1 557 561 Piscataway IEEE 2023 CFP23SAP-USB