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 13 articles )

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  1. Cai, Yunfeng; Liu, Chengyu: An algebraic approach to nonorthogonal general joint block diagonalization (2017)
  2. Nakatsukasa, Yuji; Soma, Tasuku; Uschmajew, André: Finding a low-rank basis in a matrix subspace (2017)
  3. Batselier, Kim; Wong, Ngai: Symmetric tensor decomposition by an iterative eigendecomposition algorithm (2016)
  4. Fan, H.-Y.; Zhang, L.; Chu, E.K.-w.; Wei, Y.: Q-less QR decomposition in inner product spaces (2016)
  5. Sangalli, Giancarlo; Tani, Mattia: Isogeometric preconditioners based on fast solvers for the Sylvester equation (2016)
  6. Sorber, Laurent; Domanov, Ignat; Van Barel, Marc; De Lathauwer, Lieven: Exact line and plane search for tensor optimization (2016)
  7. Van Barel, Marc: Designing rational filter functions for solving eigenvalue problems by contour integration (2016)
  8. Van Barel, Marc; Kravanja, Peter: Nonlinear eigenvalue problems and contour integrals (2016)
  9. Xi, Yuanzhe; Saad, Yousef: Computing partial spectra with least-squares rational filters (2016)
  10. Yang, Yuning; Feng, Yunlong; Huang, Xiaolin; Suykens, Johan A.K.: Rank-1 tensor properties with applications to a class of tensor optimization problems (2016)
  11. Dreesen, Philippe; Ishteva, Mariya; Schoukens, Johan: Decoupling multivariate polynomials using first-order information and tensor decompositions (2015)
  12. Macedo, Hugo Daniel; Oliveira, José: A linear algebra approach to OLAP (2015)
  13. Sørensen, Mikael; Domanov, Ignat; de Lathauwer, Lieven: Coupled canonical polyadic decompositions and (coupled) decompositions in multilinear rank-$(L_r,n,L_r,n,1)$ terms. II: Algorithms (2015)