Features
Tensor decompositions
Compute the canonical polyadic decomposition, multilinear singular value decomposition, block term decompositions and low multilinear rank approximation.
Structured data fusion
Define your own (coupled) matrix and tensor factorizations with structured factors and support for dense, sparse, incomplete and structured data sets.
Complex optimization
Quasi-Newton and nonlinear least squares optimization with complex variables including numerical complex differentiation.
Global minimization of bivariate polynomials & rational functions
Real and complex exact line search (LS) and real exact plane search (PS) for tensor optimization.
Structured tensor representations
Obtain faster tensor operations and decompositions by exploiting the structure of the data, such as Hankel, Tensor Train and CPD structure.
And much more
Tensorize data, compute higher-order statistics, visualize tensors of arbitrary order, estimate a tensor's rank or multilinear rank, ...