Tensors (also known as multidimensional arrays or N-way arrays) are
used in a variety of applications ranging from chemometrics to
psychometrics. The Tensor Toolbox provides the following classes for manipulating dense, sparse, and structured tensors using MATLAB's
- tensor - A (dense) multidimensional array
(extends MATLAB's current capabilities).
- sptensor - A sparse multidimensional array.
- tenmat - Store a tensor as a matrix, with extra information so
that it can be converted back into a tensor.
- sptenmat - Store a sptensor as sparse matrix in
coordinate format, with extra information so
that it can be converted back into a sptensor.
- ttensor - Store a tensor decomposed as a Tucker operator
- ktensor - Store a tensor decomposed as a Kruskal operator
For more details about what tensors are, see the
What's New in Version 2.4?
Version 2.4 adds optimization-based methods for calculating CP (even
with missing data). The new functions require the
Poblano Toolbox for
Matlab, available separately. To see a complete
list of changes, view the RELEASE_NOTES.txt file.
To obtain a free license for our software, visit the
Tensor Toolbox License and Registration page. Note: This code requires MATLAB2006a (version 7.2) or later.
Tensor Toolbox Mailing List to get important information on new
releases and more.
How to Cite
Cite the following references for the MATLAB Tensor Toolbox Version
- For optimization approach to CP (cp_opt code): E. Acar,
T. G. Kolda and D. M. Dunlavy,
Optimization Approach for Fitting Canonical Tensor Decompositions,
Technical Report Number SAND2009-0857, Sandia National Laboratories,
Albuquerque, NM and Livermore, CA, February 2009.
- For CP with missing data (cp_wopt code): E. Acar, D. M.
Dunlavy, T. G. Kolda and M. Mørup,
Tensor Factorizations with Missing Data. In: SDM10:
Proceedings of the 2010 SIAM International Conference on Data
Mining, SIAM, April 2010.
- For working with sparse or structured tensors: Brett W. Bader and Tamara G. Kolda,
Efficient MATLAB computations with
sparse and factored tensors, SIAM Journal on Scientific
Computing 30(1):205-231, December 2007.
- For working with dense tensors: Brett W. Bader and Tamara G. Kolda,
MATLAB Tensor Classes for Fast Algorithm Prototyping,
ACM Transactions on Mathematical
Software, 32(4), December 2006.
- Toolbox: Brett W. Bader and Tamara G. Kolda, MATLAB Tensor Toolbox
http://www.sandia.gov/~tgkolda/TensorToolbox/, March 2010.
Cite the following references for Memory-Efficient
Tucker (MET) included with
the MATLAB Tensor Toolbox Version 2.4:
Click here to see more Tensor Toolbox
papers, including user contributions.
Questions or Comments
Links to Previous Versions
Tensor objects in MATLAB — The tensor
toolbox allows for the manipulation of multiway arrays.
The N-way toolbox for MATLAB