semvec/pypi-packages huggingface dataset:
pyfaust-torch 3.42.2
FAµST python toolbox
License: BSD-3-clause

[![FAµST logo](https://faust.inria.fr/files/2023/10/faust_logo-tight-450x110-1.jpg)](https://faust.inria.fr)
![pipeline status](https://gitlab.inria.fr/faustgrp/faust/badges/main/pipeline.svg?ignore_skipped=true)
[![coverage](https://gitlab.inria.fr/faustgrp/faust/badges/hakim_branch/coverage.svg?job=pyfaust_test_code_coverage&key_text=coverage)](https://gitlab.inria.fr/faustgrp/faust/-/jobs/artifacts/hakim_branch/browse/htmlcov?job=pyfaust_test_code_coverage)
![python3](https://img.shields.io/pypi/pyversions/pyfaust)

The FAµST toolbox provides algorithms and data structures to decompose a given dense matrix into a product of sparse matrices in order to reduce its computational complexity (both for storage and manipulation).

FaµST can be used to:

- speed up / reduce the memory footprint of iterative algorithms commonly used for solving high dimensional linear inverse problems,
- learn dictionaries with an intrinsically efficient implementation,
- compute (approximate) fast Fourier transforms on graphs.
pyfaust-openblaso 3.42.2
FAµST python toolbox
License: BSD-3-clause

[![FAµST logo](https://faust.inria.fr/files/2023/10/faust_logo-tight-450x110-1.jpg)](https://faust.inria.fr)
![pipeline status](https://gitlab.inria.fr/faustgrp/faust/badges/main/pipeline.svg?ignore_skipped=true)
[![coverage](https://gitlab.inria.fr/faustgrp/faust/badges/hakim_branch/coverage.svg?job=pyfaust_test_code_coverage&key_text=coverage)](https://gitlab.inria.fr/faustgrp/faust/-/jobs/artifacts/hakim_branch/browse/htmlcov?job=pyfaust_test_code_coverage)
![python3](https://img.shields.io/pypi/pyversions/pyfaust)

The FAµST toolbox provides algorithms and data structures to decompose a given dense matrix into a product of sparse matrices in order to reduce its computational complexity (both for storage and manipulation).

FaµST can be used to:

- speed up / reduce the memory footprint of iterative algorithms commonly used for solving high dimensional linear inverse problems,
- learn dictionaries with an intrinsically efficient implementation,
- compute (approximate) fast Fourier transforms on graphs.
pyfaust 3.42.2
FAµST python toolbox
License: BSD-3-clause

[![FAµST logo](https://faust.inria.fr/files/2023/10/faust_logo-tight-450x110-1.jpg)](https://faust.inria.fr)
![pipeline status](https://gitlab.inria.fr/faustgrp/faust/badges/main/pipeline.svg?ignore_skipped=true)
[![coverage](https://gitlab.inria.fr/faustgrp/faust/badges/hakim_branch/coverage.svg?job=pyfaust_test_code_coverage&key_text=coverage)](https://gitlab.inria.fr/faustgrp/faust/-/jobs/artifacts/hakim_branch/browse/htmlcov?job=pyfaust_test_code_coverage)
![python3](https://img.shields.io/pypi/pyversions/pyfaust)

The FAµST toolbox provides algorithms and data structures to decompose a given dense matrix into a product of sparse matrices in order to reduce its computational complexity (both for storage and manipulation).

FaµST can be used to:

- speed up / reduce the memory footprint of iterative algorithms commonly used for solving high dimensional linear inverse problems,
- learn dictionaries with an intrinsically efficient implementation,
- compute (approximate) fast Fourier transforms on graphs.