Compact implementation of basic operations in the tensor-train (TT) format, including TT-SVD, TT-ALS, TT-ANOVA, TT-cross, TT-truncate, etc.
Project description
teneva
Description
This python package, named teneva (tensor evaluation), provides a very compact implementation of basic operations in the tensor-train (TT) format, including TT-SVD, TT-ALS, TT-ANOVA, TT-cross, TT-truncate, "add", "mul", "norm", "mean", etc. The program code is organized within a functional paradigm and it is very easy to learn and use.
Installation
The package can be installed via pip: pip install teneva (it requires the Python programming language of the version >= 3.6). It can be also downloaded from the repository teneva and installed by python setup.py install command from the root folder of the project.
Required python packages numpy, scipy and numba will be automatically installed during the installation of the main software product.
Examples
- See the colab notebook teneva_demo with brief description and demonstration of the capabilities of each function from the
tenevapackage, including the basic examples of using of the TT-ALS, TT-ANOVA and TT-cross for multidimensional function approximation (we approximate the 100 dimensional Rosenbrock function for the simple demonstration).
Tutorials
- Colab-notebook Tensor train basics with a detailed description of the specific features of the tensor train decomposition and demos.
- Colab-notebook Build and truncate the tensor train with a description of the method for constructing a TT-decomposition for a given tensor (TT-SVD algorithm) and a method for additional rounding (compression, truncation) of the TT-decomposition, including program code and numerical examples.
- Colab-notebook Maxvol and Maxvol_rect algorithms with a detailed description of the
maxvolalgorithm to efficiently find the maximum volume submatrix in a given matrix, its program code (including jax-draft) and demo examples. - Colab-notebook Black box approximation with tensor train with examples for multidimensional function (black box) approximation with the TT-ALS, TT-ANOVA and TT-cross approaches.
Authors
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
