The DOI matches its Crossref record; 144 of the 144 references it cites were found in a registry or an index.
A literature survey of low‐rank tensor approximation techniques
Abstract
During the last years, low‐rank tensor approximation has been established as a new tool in scientific computing to address large‐scale linear and multilinear algebra problems, which would be intractable by classical techniques. This survey attempts to give a literature overview of current developments in this area, with an emphasis on function‐related tensors. (© 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Horn, R. A., & Johnson, C. R. (1985) · Cited by 22,203
Tensor Decompositions and Applications
Kolda, T. G., & Bader, B. W. (2009) · SIAM Review · doi:10.1137/07070111x · Cited by 10,301
Density matrix formulation for quantum renormalization groups
White, S. R. (1992) · Physical Review Letters · doi:10.1103/physrevlett.69.2863 · Cited by 7,702
The density-matrix renormalization group in the age of matrix product states
Schollwöck, U. (2011) · Annals of Physics · doi:10.1016/j.aop.2010.09.012 · Cited by 4,818
A Multilinear Singular Value Decomposition
De Lathauwer, L., De Moor, B., & Vandewalle, J. (2000) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/s0895479896305696 · Cited by 4,106
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Oseledets, I. V. (2011) · SIAM Journal on Scientific Computing · doi:10.1137/090752286 · Cited by 2,636
Efficient Classical Simulation of Slightly Entangled Quantum Computations
Vidal, G. (2003) · Physical Review Letters · doi:10.1103/physrevlett.91.147902 · Cited by 2,368
Tensor Completion for Estimating Missing Values in Visual Data
Liu, J., Musialski, P., Wonka, P., & Ye, J. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.39 · Cited by 2,063
Cited by
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021) · arXiv · doi:10.48550/arxiv.2106.09685 · Cited by 13,453
Chen, C. L. P., & Liu, Z. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2716952 · Cited by 1,957
Tensor Decomposition for Signal Processing and Machine Learning
Sidiropoulos, N. D., De Lathauwer, L., Fu, X., Huang, K., Papalexakis, E. E., & Faloutsos, C. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2690524 · Cited by 1,617
DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications
Huynh, L. N., Lee, Y., & Balan, R. K. (2017) · Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3081333.3081360 · Cited by 329
Tensor product methods and entanglement optimization for ab initio quantum chemistry
Szalay, S., Pfeffer, M., Murg, V., Barcza, G., Verstraete, F., Schneider, R., & Legeza, Ö. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24898 · Cited by 314
Metadata
- Authors
- Grasedyck, L., Kressner, D., & Tobler, C.
- Venue
- GAMM-Mitteilungen
- Publisher
- Wiley
- Published
- 2013-08
- Volume
- 36
- Issue
- 1
- Pages
- 53-78
- ISSN
- 0936-7195 (ISSN-L)
- 1522-2608
- OpenAlex
- W1967077133
- Cited by
- 577
- References
- 144
- FWCI
- 14.80
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
