https://github.com/Bihaqo/t3f

Tensor Train decomposition on TensorFlow
https://github.com/Bihaqo/t3f

matrix-product-states tensor-train tensorflow

Last synced: 3 months ago
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Tensor Train decomposition on TensorFlow

• Host: GitHub
• URL: https://github.com/Bihaqo/t3f
• Owner: Bihaqo
• License: mit
• Created: 2017-01-19T17:10:30.000Z (almost 8 years ago)
• Default Branch: develop
• Last Pushed: 2021-04-06T16:50:31.000Z (over 3 years ago)
• Last Synced: 2024-07-04T02:15:44.163Z (4 months ago)
• Topics: matrix-product-states, tensor-train, tensorflow
• Language: Python
• Homepage: https://t3f.readthedocs.io/en/latest/index.html
• Size: 1.54 MB
• Stars: 218
• Watchers: 14
• Forks: 56
• Open Issues: 58
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
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TensorFlow implementation of a library for working with Tensor Train (TT) decomposition which is also known as Matrix Product State (MPS).

# Documentation

The documentation is available via [readthedocs](https://t3f.readthedocs.io/en/latest/index.html).

# Comparison with other libraries

There are about a dozen other libraries implementing Tensor Train decomposition. 

The main difference between `t3f` and other libraries is that `t3f` has extensive support for Riemannian optimization and that it uses TensorFlow as backend and thus supports GPUs, automatic differentiation, and batch processing. For a more detailed comparison with other libraries, see the [corresponding page](https://t3f.readthedocs.io/en/latest/comparison.html) in the docs.

# Tests

```bash

nosetests  --logging-level=WARNING

```

# Building documentation

The documentation is build by sphinx and hosted on readthedocs.org. To locally rebuild the documentation, install sphinx and compile the docs by

```bash

cd docs

make html

```

# Citing

If you use T3F in your research work, we kindly ask you to cite [the paper](http://jmlr.org/papers/v21/18-008.html) describing this library

```

@article{JMLR:v21:18-008,

  author  = {Alexander Novikov and Pavel Izmailov and Valentin Khrulkov and Michael Figurnov and Ivan Oseledets},

  title   = {Tensor Train Decomposition on TensorFlow (T3F)},

  journal = {Journal of Machine Learning Research},

  year    = {2020},

  volume  = {21},

  number  = {30},

  pages   = {1-7},

  url     = {http://jmlr.org/papers/v21/18-008.html}

}

```