New release

GUDHI version 3.10.0

The GUDHI library now offers a persistence matrix module in C++ and Rips complex persistence scikit-learn like interface in Python

We are pleased to announce the release 3.10.0 of the GUDHI library.

We are now using GitHub to develop the GUDHI library, do not hesitate to fork the GUDHI project on GitHub. From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz).

Below is a list of changes made since GUDHI 3.9.0:

  • Persistence matrix

    Matrix API is in a beta version and may change in incompatible ways in the near future.

    • Matrix structure for filtered complexes with multiple functionnalities related to persistence homology, such as representative cycles computation or vineyards.
  • Rips complex
    • Rips complex persistence scikit-learn like interface
  • Čech complex
    • A new utility to compute the Delaunay-Čech filtration on a Delaunay triangulation.
  • Installation
    • CGAL ≥ 5.1.0 is now required (was ≥ 4.11.0).
    • Eigen3 ≥ 3.3.0 is now required (was ≥ 3.1.0).
  • Maintenance
    • Some bug fix for CGAL ≥ 6.0, NumPy ≥ 2.0, Scikit-learn ≥ 1.4, Matplotlib ≥ 3.6 and TensorFlow ≥ 2.16.
  • Miscellaneous

All modules are distributed under the terms of the MIT license. However, there are still GPL dependencies for many modules. We invite you to check our license dedicated web page for further details.

We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.

We provide bibtex entries for the modules of the User and Reference Manual, as well as for publications directly related to the GUDHI library.

Feel free to contact us in case you have any questions or remarks.

For further information about downloading and installing the library (C++ or Python), please visit the GUDHI web site.

GUDHI release