We are pleased to announce the release of pyTensorlab, a Python package for advanced tensor computations and complex-valued optimization.
The initial release of pyTensorlab provides a broad collection of algorithms for computing the canonical polyadic decomposition (CPD), multilinear singular value decomposition (MLSVD/HOSVD), and tensor-train (TT) decomposition for dense, sparse, incomplete, or structured tensors. The package also includes numerous auxiliary tools for tensor-tensor and tensor-matrix products, folding/unfolding operations, tensorization techniques, tensor generation, and visualization. Both real and complex data are supported through a built-in complex optimization framework. A detailed API reference is available at: https://pytensorlab.net/docs/reference/
pyTensorlab can be installed via PyPI: https://pypi.org/project/pyTensorlab/. A collection of demonstrations showcasing the use of pyTensorlab in various applications is available at https://gitlab.esat.kuleuven.be/tensorgroup-public/pytensorlab-demos. An extensive user guide and reference manual can be found at https://pytensorlab.net/docs. These resources are also accessible through the project website https://pytensorlab.net/.
pyTensorlab is the latest addition to the Tensorlab family. Tensorlab 3.0 (https://tensorlab.net<https://tensorlab.net/>) is a MATLAB toolbox offering similar functionality, including an extensive structured data fusion framework and support for block-term decompositions. Tensorlab+ (https://tensorlabplus.net<https://tensorlabplus.net/>) is a reproducible research repository built on top of Tensorlab, providing all algorithms, code, and data required to reproduce published experiments, as well as tutorials and demos showing how the proposed algorithms can be used in your own applications.
The package is freely available for non-commercial research use. We hope you find pyTensorlab valuable for your work, and we welcome any suggestions or feedback.
Best regards,
The pyTensorlab Team
URL: https://pytensorlab.net<https://pytensorlab.net/>
E-mail: pytensorlab@esat.kuleuven.be
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