Metadata-Version: 2.1
Name: teneva
Version: 0.7.0
Summary: Fast multidimensional cross approximation in the tensor-train (TT) format and related operations with TT-tensors.
Home-page: https://github.com/AndreiChertkov/teneva
Author: Andrei Chertkov
Author-email: a.chertkov@skoltech.ru
License: UNKNOWN
Project-URL: Source, https://github.com/AndreiChertkov/teneva
Description: # teneva
        
        
        ## Description
        
        This python package, named **teneva** (**ten**sor **eva**luation), provides very compact implementation for the multidimensional cross approximation algorithm in the tensor-train (TT) format.
        This package also contains a function for quickly calculating (using numba package) the values of the constructed low-rank tensor approximation, as well as a number of auxiliary useful utilities for rounding, adding, multiplying TT-tensors, etc.
        
        **Notes**:
        
        - This compact implementation does not require a fortran compiler to be installed, unlike the original [ttpy](https://github.com/oseledets/ttpy) python package.
        - The program code is organized within a functional paradigm. Most functions take `Y` - a list of the TT-cores (3D numpy arrays) - as an input argument and return its updated representation as a new list of TT-cores or some related scalar values (mean, norm, etc.).
        
        
        ## Installation
        
        The package (it requires the [Python](https://www.python.org) programming language of the version >= 3.6) can be installed via pip: `pip install teneva`. It can be also downloaded from the repository [teneva](https://github.com/AndreiChertkov/teneva) and installed by `python setup.py install` command from the root folder of the project.
        
        > Required python packages [numpy](https://numpy.org), [scipy](https://www.scipy.org) and [numba](https://github.com/numba/numba) will be automatically installed during the installation of the main software product.
        
        
        ## Examples
        
        See `demo/demo.py` script, which contains code for approximation of the multivariate (100 dimensional) Rosenbrock function with noise on a uniform grid by various methods (TT-ANOVA, TT-ALS, TT-Cross) and by its combinations.
        
        
        ## Tutorials
        
        > All materials at the moment are presented in the form of drafts and are written in Russian.
        
        - Colab-ноутбук [Разложение тензорного поезда](https://colab.research.google.com/drive/1TR-ptUINvglasplQCLXdl2g0F3Nh5AIG?usp=sharing) с подробным описанием специфических особенностей разложения тензорного поезда и демонстрационными примерами.
        - Colab-ноутбук [Построение тензорного поезда и округление](https://colab.research.google.com/drive/17yW1ILOTgf1lvJEqUrn6YcHki-WYCozN?usp=sharing) с описанием метода построения TT-разложения для заданного тензора (алгоритм TT-SVD) и метода дополнительного округления (сжатия) TT-разложения, включая программный код и численные примеры.
        - Colab-ноутбук [Алгоритмы maxvol и rect_maxvol](https://colab.research.google.com/drive/186ig_CS7RA5WVRwBPzT7Wu-vwKXZrm7m?usp=sharing) с подробным описанием алгоритма `maxvol`, его программным кодом (в том числе на jax) и демонстрационными примерами.
        - Colab-ноутбук [Алгоритм TT-cross](https://colab.research.google.com/drive/1zfqwAdHAOiSbbgpPOiufmXgoErukhq4N?usp=sharing) с подробным описанием алгоритма `TT-cross`, его программным кодом и демонстрационными примерами.
        - Colab-ноутбук [Алгоритм TT-als](https://colab.research.google.com/drive/1EOAkmwkFcswCGroSvUBaXjgPDZGkkkvJ?usp=sharing) с описанием алгоритма `TT-ALS`, его программным кодом и демонстрационными примерами.
        
Keywords: low-rank representation tensor train format TT-decomposition cross approximation
Platform: UNKNOWN
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
Classifier: Framework :: Jupyter
Requires-Python: >=3.6
Description-Content-Type: text/markdown
