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 57 articles )

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  1. Che, Maolin; Wei, Yimin; Yan, Hong: Randomized algorithms for the low multilinear rank approximations of tensors (2021)
  2. Redman, William T.: On Koopman mode decomposition and tensor component analysis (2021)
  3. 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)
  4. 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)
  5. Xiao, Chuanfu; Yang, Chao; Li, Min: Efficient alternating least squares algorithms for low multilinear rank approximation of tensors (2021)
  6. Ceruti, Gianluca; Lubich, Christian: Time integration of symmetric and anti-symmetric low-rank matrices and Tucker tensors (2020)
  7. Che, Maolin; Wei, Yimin; Yan, Hong: The computation of low multilinear rank approximations of tensors via power scheme and random projection (2020)
  8. Chi, Eric C.; Gaines, Brian J.; Sun, Will Wei; Zhou, Hua; Yang, Jian: Provable convex co-clustering of tensors (2020)
  9. Domanov, Ignat; De Lathauwer, Lieven: On uniqueness and computation of the decomposition of a tensor into multilinear rank-((1,L_r,L_r)) terms (2020)
  10. Loli, Gabriele; Montardini, Monica; Sangalli, Giancarlo; Tani, Mattia: An efficient solver for space-time isogeometric Galerkin methods for parabolic problems (2020)
  11. Mao, Xianpeng; Yuan, Gonglin; Yang, Yuning: A self-adaptive regularized alternating least squares method for tensor decomposition problems (2020)
  12. Montardini, Monica; Negri, Matteo; Sangalli, Giancarlo; Tani, Mattia: Space-time least-squares isogeometric method and efficient solver for parabolic problems (2020)
  13. Nie, Jiawang; Yang, Zi: Hermitian tensor decompositions (2020)
  14. Usevich, Konstantin; Dreesen, Philippe; Ishteva, Mariya: Decoupling multivariate polynomials: interconnections between tensorizations (2020)
  15. Yang, Yuning: The epsilon-alternating least squares for orthogonal low-rank tensor approximation and its global convergence (2020)
  16. Beltrán, Carlos; Breiding, Paul; Vannieuwenhoven, Nick: Pencil-based algorithms for tensor rank decomposition are not stable (2019)
  17. Cai, Yunfeng; Li, Ren-Cang: Perturbation analysis for matrix joint block diagonalization (2019)
  18. Che, Maolin; Wei, Yimin: Randomized algorithms for the approximations of Tucker and the tensor train decompositions (2019)
  19. Feng, Yuehua; Xiao, Jianwei; Gu, Ming: Flip-flop spectrum-revealing QR factorization and its applications to singular value decomposition (2019)
  20. Hauenstein, Jonathan D.; Oeding, Luke; Ottaviani, Giorgio; Sommese, Andrew J.: Homotopy techniques for tensor decomposition and perfect identifiability (2019)

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