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 48 articles )

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  1. Ceruti, Gianluca; Lubich, Christian: Time integration of symmetric and anti-symmetric low-rank matrices and Tucker tensors (2020)
  2. Che, Maolin; Wei, Yimin; Yan, Hong: The computation of low multilinear rank approximations of tensors via power scheme and random projection (2020)
  3. 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)
  4. Mao, Xianpeng; Yuan, Gonglin; Yang, Yuning: A self-adaptive regularized alternating least squares method for tensor decomposition problems (2020)
  5. Montardini, Monica; Negri, Matteo; Sangalli, Giancarlo; Tani, Mattia: Space-time least-squares isogeometric method and efficient solver for parabolic problems (2020)
  6. Usevich, Konstantin; Dreesen, Philippe; Ishteva, Mariya: Decoupling multivariate polynomials: interconnections between tensorizations (2020)
  7. Beltrán, Carlos; Breiding, Paul; Vannieuwenhoven, Nick: Pencil-based algorithms for tensor rank decomposition are not stable (2019)
  8. Cai, Yunfeng; Li, Ren-Cang: Perturbation analysis for matrix joint block diagonalization (2019)
  9. Che, Maolin; Wei, Yimin: Randomized algorithms for the approximations of Tucker and the tensor train decompositions (2019)
  10. Feng, Yuehua; Xiao, Jianwei; Gu, Ming: Flip-flop spectrum-revealing QR factorization and its applications to singular value decomposition (2019)
  11. Hauenstein, Jonathan D.; Oeding, Luke; Ottaviani, Giorgio; Sommese, Andrew J.: Homotopy techniques for tensor decomposition and perfect identifiability (2019)
  12. Kaihnsa, Nidhi; Kummer, Mario; Plaumann, Daniel; Namin, Mahsa Sayyary; Sturmfels, Bernd: Sixty-four curves of degree six (2019)
  13. Pfeffer, Max; Seigal, Anna; Sturmfels, Bernd: Learning paths from signature tensors (2019)
  14. Sørensen, Mikael; De Lathauwer, Lieven: Fiber sampling approach to canonical polyadic decomposition and application to tensor completion (2019)
  15. Vervliet, Nico; Debals, Otto; De Lathauwer, Lieven: Exploiting efficient representations in large-scale tensor decompositions (2019)
  16. A, Suganya; Dharma, Dejey: Compact video content representation for video coding using low multi-linear tensor rank approximation with dynamic core tensor order (2018)
  17. Breiding, Paul; Vannieuwenhoven, Nick: The condition number of join decompositions (2018)
  18. Breiding, Paul; Vannieuwenhoven, Nick: A Riemannian trust region method for the canonical tensor rank approximation problem (2018)
  19. Cuyt, Annie; Knaepkens, Ferre; Lee, Wen-shin: From exponential analysis to Padé approximation and tensor decomposition, in one and more dimensions (2018)
  20. Kuo, Yueh-Cheng; Lee, Tsung-Lin: Computing the unique CANDECOMP/PARAFAC decomposition of unbalanced tensors by homotopy method (2018)

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