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Major Features and Improvements
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- Minimizers can use the new ``SimpleLoss.from_any`` method that allows other libraries to hook into the minimization.
For example, using zfit-physics, minimizers can directly minimize RooFit ``RooNllVar`` (as created by ``createNLL`` described `here <https://root.cern.ch/doc/master/classRooAbsPdf.html#a24b1afec4fd149e08967eac4285800de>`_
- Added the well performing ``LevenbergMarquardt`` minimizer, a new implementation of the Levenberg-Marquardt algorithm.
- New BFGS minimizer implementation of Scipy, ``ScipyBFGS``.
- Reactivate a few minimizers: ``ScipyDogleg``, ``ScipyNCG``, ``ScipyCOBYLA`` and ``ScipyNewtonCG``
Breaking changes
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- removed multiple, old deprecated methods and arguments
Deprecations
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- use ``stepsize`` instead of ``step_size`` in the ``zfit.Parameter`` constructor
Bug fixes and small changes
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- add possibility to not jit by using ``force_eager`` in ``tf.function`` or raise a ``z.DoNotCompile`` error
- ``SimpleLoss`` can now be added together with another SimpleLoss
- ``get_params`` supports an ``autograd`` argument to filter parameters that do not support automatic differentiation.
An object with parameters can advertise, which parameters are differentiable (with ``autograd_params``); by default, all
parameters are assumed to be differentiable, the same effect as ``True``. If autograd is performed on parameters that
do not support it, an error is raised.
- Use ``kanah`` sum for larger likelihoods by default to improve numerical stability
- Using the same ``zfit.Parameter`` for multiple arguments (i.e. to specify a common width in a PDF with a different width
for left and right) could cause a crash due to some internal caching. This is now fixed.
- Minimizers have now been renamed without the trailing ``V1``. The old names are still available but will be removed in the future.
Experimental
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Requirement changes
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Thanks
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