A new reproducible research repository Tensorlab+ has been released. Tensorlab+ provides implementations of new algorithms, experiment files, demos and tutorials for 34 papers.


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, ...

Version History

Tensorlab 3.0 / Mar 28, 2016
Tensorlab 3.0 features dedicated algorithms for the decomposition in multilinear rank-$(L_r,L_r,1)$ terms, various tensorization techniques, a more flexible and expanded modeling language for structured data fusion problems, support for efficient representations of structured tensors in most optimization-based decomposition algorithms, and new algorithms for dealing with sparse, incomplete and/or large-scale datasets. A new visualization tool is introduced, many existing algorithms have received performance and flexibility updates, e.g., by using more lenient option parsing, and a number of bugs have been fixed. Finally, the user guide has been extended significantly and illustrated with practical demos.
Tensorlab 2.0 / Jan 31, 2014
Tensorlab 2.0 introduces the Structured Data Fusion framework and adds support for sparse and incomplete tensors. Speed improvements have been made across various methods, and a new method for low multilinear rank approximation has been added using adaptive cross-approximation. Tensorlab 2.01 and 2.02 bring various speed improvements and bug fixes.
Tensorlab 1.0 / Feb 11, 2013
First official version of Tensorlab introducing algorithms of various tensor decompositions, as well as different auxiliary tools such as multiplication methods, folding/unfolding commands and visualization techniques. Tensorlab 1.01 and 1.02 bring various speed improvements and bug fixes.