protes 0.1.1

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

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 programming language of the version >= 3.6). The jax and optax libraries should be manually installed for successful operation of jax version of the code. Alternatively, an equivalent pytorch version can be used (it is currently very slow), in this case, please, install manually pytorch library.

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 (to run the pytorch version, please, specify the appropriate argument: python demo/demo_func.py tor).

Authors

• Anastasia Batsheva
• Andrei Chertkov
• Ivan Oseledets
• Gleb Ryzhakov

Citation

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

@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}
}