Tensorlab

Tensorlab: A MATLAB Toolbox for Tensor Computations. Tensorlab is a MATLAB toolbox that offers algorithms for: structured data fusion: define your own (coupled) matrix and tensor factorizations with structured factors and support for dense, sparse and incomplete data sets, tensor decompositions: canonical polyadic decomposition (CPD), multilinear singular value decomposition (MLSVD), block term decompositions (BTD) and low multilinear rank approximation (LMLRA), complex optimization: quasi-Newton and nonlinear-least squares optimization with complex variables including numerical complex differentiation, global minimization of bivariate polynomials and rational functions: both real and complex exact line search (LS) and real exact plane search (PS) for tensor optimization, and much more: cumulants, tensor visualization, estimating a tensor’s rank or multilinear rank, …


References in zbMATH (referenced in 71 articles )

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  1. Al Daas, Hussam; Ballard, Grey; Benner, Peter: Parallel algorithms for tensor train arithmetic (2022)
  2. Khouja, Rima; Khalil, Houssam; Mourrain, Bernard: Riemannian Newton optimization methods for the symmetric tensor approximation problem (2022)
  3. Prévost, Clémence; Borsoi, Ricardo A.; Usevich, Konstantin; Brie, David; Bermudez, José C. M.; Richard, Cédric: Hyperspectral super-resolution accounting for spectral variability: coupled tensor LL1-based recovery and blind unmixing of the unknown super-resolution image (2022)
  4. Ahn, Miju; Eikmeier, Nicole; Haddock, Jamie; Kassab, Lara; Kryshchenko, Alona; Leonard, Kathryn; Needell, Deanna; Madushani, R. W. M. A.; Sizikova, Elena; Wang, Chuntian: On large-scale dynamic topic modeling with nonnegative CP tensor decomposition (2021)
  5. Batselier, Kim; Cichocki, Andrzej; Wong, Ngai: MERACLE: constructive layer-wise conversion of a tensor train into a MERA (2021)
  6. Che, Maolin; Wei, Yimin; Yan, Hong: Randomized algorithms for the low multilinear rank approximations of tensors (2021)
  7. Che, Maolin; Wei, Yimin; Yan, Hong: An efficient randomized algorithm for computing the approximate Tucker decomposition (2021)
  8. Chu, Moody T.; Lin, Matthew M.: Nonlinear power-like and SVD-like iterative schemes with applications to entangled bipartite rank-1 approximation (2021)
  9. Chu, Moody T.; Lin, Matthew M.: Nonlinear power-like and SVD-like iterative schemes with applications to entangled bipartite rank-1 approximation (2021)
  10. Liang, Chang; Yang, Yuning: Shifted eigenvalue decomposition method for computing C-eigenvalues of a piezoelectric-type tensor (2021)
  11. Luo, Yuetian; Raskutti, Garvesh; Yuan, Ming; Zhang, Anru R.: A sharp blockwise tensor perturbation bound for orthogonal iteration (2021)
  12. Oseledets, I. V.; Kharyuk, P. V.: Structuring data with block term decomposition: decomposition of joint tensors and variational block term decomposition as a parametrized mixture distribution model (2021)
  13. Redman, William T.: On Koopman mode decomposition and tensor component analysis (2021)
  14. Vanderstukken, Jeroen; De Lathauwer, Lieven: Systems of polynomial equations, higher-order tensor decompositions, and multidimensional harmonic retrieval: a unifying framework. Part I: the canonical polyadic decomposition (2021)
  15. Vanderstukken, Jeroen; Kürschner, Patrick; Domanov, Ignat; De Lathauwer, Lieven: Systems of polynomial equations, higher-order tensor decompositions, and multidimensional harmonic retrieval: a unifying framework. Part II: The block term decomposition (2021)
  16. Xiao, Chuanfu; Yang, Chao; Li, Min: Efficient alternating least squares algorithms for low multilinear rank approximation of tensors (2021)
  17. Zhang, Chi; Fanaee-T, Hadi; Thoresen, Magne: Feature extraction from unequal length heterogeneous EHR time series via dynamic time warping and tensor decomposition (2021)
  18. Ceruti, Gianluca; Lubich, Christian: Time integration of symmetric and anti-symmetric low-rank matrices and Tucker tensors (2020)
  19. Che, Maolin; Wei, Yimin; Yan, Hong: The computation of low multilinear rank approximations of tensors via power scheme and random projection (2020)
  20. Chi, Eric C.; Gaines, Brian J.; Sun, Will Wei; Zhou, Hua; Yang, Jian: Provable convex co-clustering of tensors (2020)

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