Provable Stochastic Algorithm for Large-Scale Fully-Connected Tensor Network Decomposition-Reference-Cited by-同舟云学术

Provable Stochastic Algorithm for Large-Scale Fully-Connected Tensor Network Decomposition

Published:2023-11-27 Issue:1 Volume:98 Page:
ISSN:0885-7474
Container-title:Journal of Scientific Computing
language:en
Short-container-title:J Sci Comput

Author:

Zheng Wen-Jie,Zhao Xi-Le^ORCID,Zheng Yu-Bang,Huang Ting-Zhu

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Open Research Fund Program of Data Recovery Key Laboratory of Sichuan Province

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,General Engineering,Theoretical Computer Science,Software,Applied Mathematics,Computational Mathematics,Numerical Analysis

Link

https://link.springer.com/content/pdf/10.1007/s10915-023-02404-1.pdf

Reference55 articles.

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3. Yokota, T., Zhao, Q., Cichocki, A.: Smooth PARAFAC decomposition for tensor completion. IEEE Trans. Signal Process. 64(20), 5423–5436 (2016)

4. Zeng, C.: Rank properties and computational methods for orthogonal tensor decompositions. J. Sci. Comput. 94(1), 6 (2023)

5. Pan, J., Ng, M.K., Liu, Y., Zhang, X., Yan, H.: Orthogonal nonnegative Tucker decomposition. SIAM J. Sci. Comput. 43(1), B55–B81 (2021)

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