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Merge pull request #121 from mrava87/dev
Prepare for v0.6.0
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CHANGELOG.md

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# 0.6.0
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:vertical_traffic_light: :vertical_traffic_light: This is the first release supporting PyLops v2.
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:vertical_traffic_light: :vertical_traffic_light:
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* Added ``grad`` method to ``pyproximal.utils.bilinear.LowRankFactorizedMatrix`` operator
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* Allow passing optional arguments to solvers in ``pyproximal.proximal.L2``
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* Modified codebase to integrate with pylops's ``cupy`` backend.
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* Modified codebase to integrate with ``pylops`` v2.
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# 0.5.0
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:vertical_traffic_light: :vertical_traffic_light: This is the latest release supporting PyLops v1.
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:vertical_traffic_light: :vertical_traffic_light:
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* Added ``pyproximal.proximal.Log1`` operator
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* Added ``pyproximal.proximal.Log1`` operator
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* Allow ``radius`` parameter of ``pyproximal.optimization.primal.L0`` to be a function
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* Allow ``tau`` parameter of ``pyproximal.optimization.primal.HQS`` to be a vector
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and change over iterations

docs/source/changelog.rst

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Changelog
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=========
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Version 0.6.0
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--------------
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*Released on: 18/03/2023*
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|:vertical_traffic_light:| |:vertical_traffic_light:| This is the first release supporting PyLops v2.
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|:vertical_traffic_light:| |:vertical_traffic_light:|
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* Added ``grad`` method to :py:class:`pyproximal.utils.bilinear.LowRankFactorizedMatrix` operator
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* Allow passing optional arguments to solvers in :py:class:`pyproximal.proximal.L2`
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* Modified codebase to integrate with pylops's ``cupy`` backend.
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* Modified codebase to integrate with ``pylops`` v2.
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Version 0.5.0
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--------------
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*Released on: 20/08/2022*

docs/source/installation.rst

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To install the PyProximal library you will need **Python 3.8 or greater**.
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.. note::
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Versions prior to v0.3.0 work alsi with Python 3.6 or greater, however they
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require scipy version to be lower than v1.8.0
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Versions prior to v0.3.0 require Python 3.6 or greater, and scipy version
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lower than v1.8.0.
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Dependencies
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------------

pyproximal/proximal/TV.py

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# Update divergence vectors and project
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if self.ndim == 1:
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dx = derivOp(sol)
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dx = derivOp @ sol
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r -= 1. / (4 * gamma * mt**2) * dx
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weights = np.maximum(1, np.abs(r))
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elif self.ndim == 2:

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