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Tools for the decomposition of tensors

Project description

The Tensor Toolbox provides functionalities for the decomposition of tensors in tensor-train format [1] and spectral tensor-train format [2].

The toolbox provides the following tools:

  • TT-svd

  • TT-cross

  • TT-dmrg

  • TT-vectors

  • TT-matrices

  • Quatics TT-vectors/matrices (cross and dmrg)

  • TT-integration

  • Basic operations in TT format

  • Multi-linear algebra in TT format (Steepest descent, CG and GMRES)

  • STT-construction

  • STT-projection/interpolation

  • STT for single quantities and for fields


For everything to go smooth, I suggest that you first install some dependencies separately: numpy, scipy, matplotlib can be installed by:

$ pip install numpy scipy matplotlib

If you need MPI support in the TensorToolbox, you need to have an MPI back-end installed on your machine and add the right path on the $LD_LIBRARY_PATH, so that mpi4py can link to it. You should install mpi4py manually by

$ pip install mpi4py

TensorToolbox now stores data in both cPickle files and hd5 through the python package h5py. You need then to have the necessary library package libhdf5 and libhdf5-dev, or similar on your machine. Click here for more detailed information about the manual installation of h5py.

Once the hdf5 dependency is satisfied, we can proceed further. The package depends on Cython and requires to link to an mpi backend, and find the file mpi.h. In order to manually solve the dependencies do:

$ pip install cython

$ C_INCLUDE_PATH=<path-to-mpi.h-folder> pip install h5py

When everything is set, you can install the TensorToolbox using:

$ pip install TensorToolbox

Some users might want to install the toolbox without MPI support. This is possible, but not through the pip command:

$ pip install –download=”/pth/to/downloaded/files” TensorToolbox

$ cd /pth/to/downloaded/files

$ tar xzf TensorToolbox-x.x.x.tar.gz

$ cd TensorToolbox-x.x.x

$ python install –without-mpi4py

Test Installation

You can test whether all the functionalities work by running the unit tests.

>>> import TensorToolbox
>>> TensorToolbox.RunUnitTests(maxprocs=None)

where maxprocs defines the number of processors to be used if MPI support is activated. Be patient. The number of unit tests grows with the number of functionalities implemented in the software.


Examples can be found inside the package. To find them, download the source:

$ pip install –download=”/pth/to/downloaded/files” TensorToolbox

$ cd /pth/to/downloaded/files

$ tar xzf TensorToolbox-x.x.x.tar.gz

$ cd TensorToolbox-x.x.x

$ cd Examples


[1] Oseledets, I. (2011). Tensor-train decomposition. SIAM Journal on Scientific Computing, 33(5), 2295–2317. Retrieved from

[2] Bigoni, D. and Marzouk, Y.M. and Engsig-Karup, A.P. (2014) Spectral tensor-train decomposition. (Submitted) ArXiv:

Change Log

  • Fixed bug in TensorWrapper

  • Fixed TensorWrapper unit tests

  • Added support for Python3. Updated interface to SpectralToolbox 0.2.0.

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TensorToolbox-1.0.9.tar.gz (276.0 kB view hashes)

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