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 30 articles )

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  1. Vervliet, Nico; Debals, Otto; De Lathauwer, Lieven: Exploiting efficient representations in large-scale tensor decompositions (2019)
  2. A, Suganya; Dharma, Dejey: Compact video content representation for video coding using low multi-linear tensor rank approximation with dynamic core tensor order (2018)
  3. Breiding, Paul; Vannieuwenhoven, Nick: A Riemannian trust region method for the canonical tensor rank approximation problem (2018)
  4. Cuyt, Annie; Knaepkens, Ferre; Lee, Wen-shin: From exponential analysis to Padé approximation and tensor decomposition, in one and more dimensions (2018)
  5. Kuo, Yueh-Cheng; Lee, Tsung-Lin: Computing the unique CANDECOMP/PARAFAC decomposition of unbalanced tensors by homotopy method (2018)
  6. Li, Zhening; Nakatsukasa, Yuji; Soma, Tasuku; Uschmajew, André: On orthogonal tensors and best rank-one approximation ratio (2018)
  7. Massarenti, Alex; Mella, Massimiliano; Staglianò, Giovanni: Effective identifiability criteria for tensors and polynomials (2018)
  8. Springer, Paul; Bientinesi, Paolo: Design of a high-performance GEMM-like tensor-tensor multiplication (2018)
  9. Telen, Simon; Mourrain, Bernard; Barel, Marc Van: Solving polynomial systems via truncated normal forms (2018)
  10. Xu, Yangyang: On the convergence of higher-order orthogonal iteration (2018)
  11. Batselier, Kim; Wong, Ngai: A constructive arbitrary-degree Kronecker product decomposition of tensors. (2017)
  12. Cai, Yunfeng; Liu, Chengyu: An algebraic approach to nonorthogonal general joint block diagonalization (2017)
  13. Hashemi, Behnam; Trefethen, Lloyd N.: Chebfun in three dimensions (2017)
  14. Jüttler, Bert; Mokriš, Dominik: Low rank interpolation of boundary spline curves (2017)
  15. Nakatsukasa, Yuji; Soma, Tasuku; Uschmajew, André: Finding a low-rank basis in a matrix subspace (2017)
  16. Rauhut, Holger; Schneider, Reinhold; Stojanac, Željka: Low rank tensor recovery via iterative hard thresholding (2017)
  17. Tani, Mattia: A preconditioning strategy for linear systems arising from nonsymmetric schemes in isogeometric analysis (2017)
  18. Batselier, Kim; Wong, Ngai: Symmetric tensor decomposition by an iterative eigendecomposition algorithm (2016)
  19. Beik, Fatemeh Panjeh Ali; Saberi Movahed, Farid; Ahmadi-Asl, Salman: On the Krylov subspace methods based on tensor format for positive definite Sylvester tensor equations. (2016)
  20. Fan, H.-Y.; Zhang, L.; Chu, E. K.-w.; Wei, Y.: Q-less QR decomposition in inner product spaces (2016)

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