SciPy 1.16.0 Release Notes#
Note
SciPy 1.16.0 is not released yet!
SciPy 1.16.0 is the culmination of X months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with python -Wd and check for DeprecationWarning s).
Our development attention will now shift to bug-fix releases on the
1.16.x branch, and on adding new features on the main branch.
This release requires Python 3.11-3.13 and NumPy 1.26.4 or greater.
Highlights of this release#
scipy.optimizenow uses the new Python implementation from the PRIMA package for COBYLA. The PRIMA implementation fixes many bugs in the old Fortran 77 implementation with a better performance on average.
New features#
scipy.cluster improvements#
scipy.differentiate improvements#
scipy.interpolate improvements#
scipy.linalg improvements#
scipy.ndimage improvements#
scipy.optimize improvements#
COBYLA was updated to use the new Python implementation from the PRIMA package. The PRIMA implementation fixes many bugs in the old Fortran 77 implementation. In addition, it results in fewer function evaluations on average, but it depends on the problem and for some problems it can result in more function evaluations or a less optimal result. For those cases the user can try modifying the initial and final trust region radii given by rhobeg and tol respectively. A larger rhobeg can help the algorithm take bigger steps initially, while a smaller tol can help it for keep going and find a better solution. For more information, see the PRIMA documentation.