Skip to main content

FAµST python toolbox

Project description

FAµST logo pipeline status coverage python3

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.

Project details


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.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pyfaust_torch-3.42.1-cp311-cp311-macosx_10_9_x86_64.whl (5.4 MB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

File details

Details for the file pyfaust_torch-3.42.1-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyfaust_torch-3.42.1-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 be2e5e08836b66e899f7fe4f1c8987d97d8d8e32bf298894516de7eb0c85ffd1
MD5 4e3288af12308f3e82dc19159a247bf6
BLAKE2b-256 34857b4e18a313a1c924841d0f7f15134c2c0bca08f2d846c4fbe40f92daa4ab

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page