Metadata-Version: 2.1
Name: protes
Version: 0.1.0
Summary: Method PROTES (PRobability Optimizer with TEnsor Sampling) for optimization of the multidimensional arrays and  discretized multivariable functions based on the tensor train (TT) format
Home-page: https://github.com/anabatsh/PROTES
Author: Andrei Chertkov
Author-email: andre.chertkov@gmail.com
License: MIT
Project-URL: Source, https://github.com/anabatsh/PROTES
Keywords: Derivative-free optimization multidimensional optimization low-rank representation tensor train format
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
Description-Content-Type: text/markdown

# PROTES


## Description

Method PROTES (PRobability Optimizer with TEnsor Sampling) for optimization of the multidimensional arrays and  discretized multivariable functions based on the tensor train (TT) format.


## Installation

The package can be installed via pip: `pip install protes` (it requires the [Python](https://www.python.org) programming language of the version >= 3.6). The [jax](https://github.com/google/jax) and [optax](https://github.com/deepmind/optax) libraries should be manually installed for successful operation.


## Documentation and examples

Please see the documentation for function `protes` with a detailed description of all optimizer parameters. Examples are presented in the `demo` folder. A simple demo can be run in the console with a command `python demo/demo_func.py`.


## Authors

- [Anastasia Batsheva](https://github.com/anabatsh)
- [Andrei Chertkov](https://github.com/AndreiChertkov)
- [Ivan Oseledets](https://github.com/oseledets)
- [Gleb Ryzhakov](https://github.com/G-Ryzhakov)


## Citation

If you find our approach and/or code useful in your research, please consider citing:

```bibtex
@article{batsheva2023protes,
    author    = {Batsheva, Anastasia and Chertkov, Andrei  and Ryzhakov, Gleb and Oseledets, Ivan},
    year      = {2023},
    title     = {PROTES: Probabilistic Optimization with Tensor Sampling},
    journal   = {arXiv preprint arXiv:2301.12162},
    url       = {https://arxiv.org/pdf/2301.12162.pdf}
}
```
