# teneva_jax
## Description
This python package, named **teneva_jax** (**ten**sor **eva**luation with **jax**), provides a very compact implementation of basic operations in the low rank tensor-train (TT) format with jax framework for approximation, optimization and sampling with multidimensional arrays and multivariate functions. The program code is organized within a functional paradigm and it is very easy to learn and use. Each function has detailed documentation and various usage demos.
> Please, see also our github repository [teneva](https://github.com/AndreiChertkov/teneva), which contains the basic ("numpy") version of the code.
## Installation
> Current version "0.1.1".
The package can be installed via pip: `pip install teneva` (it requires the [Python](https://www.python.org) programming language of the version >= 3.8). It can be also downloaded from the repository [teneva_jax](https://github.com/AndreiChertkov/teneva_jax) and installed by `python setup.py install` command from the root folder of the project.
> Required python package ["jax[cpu]"](https://github.com/google/jax) (0.4.6+) will be automatically installed during the installation of the main software product. However, it is recommended that you manually install it first.
## Documentation, examples and tests
- See detailed [online documentation](https://teneva-jax.readthedocs.io) for a description of each function and various numerical examples for each function.
- See the jupyter notebooks in the `demo` folder with brief description and demonstration of the capabilities of each function from the `teneva_jax` package. Note that all examples from this folder are also presented in the online documentation.
## Authors
- [Andrei Chertkov](https://github.com/AndreiChertkov)
- [Gleb Ryzhakov](https://github.com/G-Ryzhakov)
- [Ivan Oseledets](https://github.com/oseledets)
> ✭__🚂 The stars that you give to **teneva_jax**, motivate us to develop faster and add new interesting features to the code 😃
## Citation
If you find our approach and/or code useful in your research, please consider citing:
```bibtex
@article{chertkov2023black,
author = {Chertkov, Andrei and Ryzhakov, Gleb and Oseledets, Ivan},
year = {2023},
title = {Black box approximation in the tensor train format initialized by ANOVA decomposition},
journal = {arXiv preprint arXiv:2208.03380 (accepted into the SIAM Journal on Scientific Computing)},
doi = {10.48550/ARXIV.2208.03380},
url = {https://arxiv.org/abs/2208.03380}
}
```
```bibtex
@article{chertkov2022optimization,
author = {Chertkov, Andrei and Ryzhakov, Gleb and Novikov, Georgii and Oseledets, Ivan},
year = {2022},
title = {Optimization of functions given in the tensor train format},
journal = {arXiv preprint arXiv:2209.14808},
doi = {10.48550/ARXIV.2209.14808},
url = {https://arxiv.org/abs/2209.14808}
}
```
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"description": "# teneva_jax\n\n\n## Description\n\nThis python package, named **teneva_jax** (**ten**sor **eva**luation with **jax**), provides a very compact implementation of basic operations in the low rank tensor-train (TT) format with jax framework for approximation, optimization and sampling with multidimensional arrays and multivariate functions. The program code is organized within a functional paradigm and it is very easy to learn and use. Each function has detailed documentation and various usage demos.\n\n> Please, see also our github repository [teneva](https://github.com/AndreiChertkov/teneva), which contains the basic (\"numpy\") version of the code.\n\n\n## Installation\n\n> Current version \"0.1.1\".\n\nThe package can be installed via pip: `pip install teneva` (it requires the [Python](https://www.python.org) programming language of the version >= 3.8). It can be also downloaded from the repository [teneva_jax](https://github.com/AndreiChertkov/teneva_jax) and installed by `python setup.py install` command from the root folder of the project.\n\n> Required python package [\"jax[cpu]\"](https://github.com/google/jax) (0.4.6+) will be automatically installed during the installation of the main software product. However, it is recommended that you manually install it first.\n\n\n## Documentation, examples and tests\n\n- See detailed [online documentation](https://teneva-jax.readthedocs.io) for a description of each function and various numerical examples for each function.\n- See the jupyter notebooks in the `demo` folder with brief description and demonstration of the capabilities of each function from the `teneva_jax` package. Note that all examples from this folder are also presented in the online documentation.\n\n\n## Authors\n\n- [Andrei Chertkov](https://github.com/AndreiChertkov)\n- [Gleb Ryzhakov](https://github.com/G-Ryzhakov)\n- [Ivan Oseledets](https://github.com/oseledets)\n\n> \u272d__\ud83d\ude82 The stars that you give to **teneva_jax**, motivate us to develop faster and add new interesting features to the code \ud83d\ude03\n\n\n## Citation\n\nIf you find our approach and/or code useful in your research, please consider citing:\n\n```bibtex\n@article{chertkov2023black,\n author = {Chertkov, Andrei and Ryzhakov, Gleb and Oseledets, Ivan},\n year = {2023},\n title = {Black box approximation in the tensor train format initialized by ANOVA decomposition},\n journal = {arXiv preprint arXiv:2208.03380 (accepted into the SIAM Journal on Scientific Computing)},\n doi = {10.48550/ARXIV.2208.03380},\n url = {https://arxiv.org/abs/2208.03380}\n}\n```\n\n```bibtex\n@article{chertkov2022optimization,\n author = {Chertkov, Andrei and Ryzhakov, Gleb and Novikov, Georgii and Oseledets, Ivan},\n year = {2022},\n title = {Optimization of functions given in the tensor train format},\n journal = {arXiv preprint arXiv:2209.14808},\n doi = {10.48550/ARXIV.2209.14808},\n url = {https://arxiv.org/abs/2209.14808}\n}\n```\n",
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