Skip to main content

Computing the Gromov–Hausdorff distance

Project description

dGH

Computes the Gromov–Hausdorff distance $d_\text{GH}(X, Y)$ by solving (a parametric family of) quadratic minimizations with affine constraints, whose solutions are guaranteed to deliver $d_\text{GH}(X, Y)$ for sufficiently large value of the parameter $c$. The minimizations are solved using the Frank-Wolfe algorithm in $O(n^3)$ time per its iteration, where $n = |X| + |Y|$ is the total number of points. Even when the algorithm fails to find a global minimum, the resulting solution provides an upper bound for $d_\text{GH}(X, Y)$.

Project details


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

dgh-0.0.1-py3-none-any.whl (4.3 kB view details)

Uploaded Python 3

File details

Details for the file dgh-0.0.1-py3-none-any.whl.

File metadata

  • Download URL: dgh-0.0.1-py3-none-any.whl
  • Upload date:
  • Size: 4.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.10

File hashes

Hashes for dgh-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 4af15ff394f41dea6efb7f3b7ac5b58fa21b50b2afa23fbd4115efa6124d800f
MD5 37361c040cd151a833d3241d52771960
BLAKE2b-256 8addec627b5ad622c6a98d27ece8f4c248bb5ee65e984b110b264de034085973

See more details on using hashes here.

Supported by

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