Pyunlocbox

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Mostly a maintenance release. Much cleaning happened and a conda package is now
available in conda-forge. Moreover, the package can now be tried online thanks
to binder.

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Development status updated from Alpha to Beta.

New features:

* Acceleration module, decoupling acceleration strategies from the solvers

* Backtracking scheme
* FISTA acceleration
* FISTA with backtracking
* Regularized non-linear acceleration (RNA)

* Solvers: gradient descent algorithm

Bug fix:

* Decrease dimensionality of variables in Douglas Rachford tutorial to reduce
test time and timeout on Travis CI.

Infrastructure:

* Continuous integration: dropped 3.3 (matplotlib dropped it), added 3.6
* We don't build PDF documentation anymore. Less burden, HTML can be downloaded
from readthedocs.

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New feature:

* Monotone+Lipschitz forward-backward-forward primal-dual algorithm (MLFBF)

Bug fix:

* Plots generated when building documentation (not stored in the repository)

Infrastructure:

* Continuous integration: dropped 2.6 and 3.2, added 3.5
* Travis-ci: check style and build doc
* Removed tox config (too cumbersome to use on dev box)
* Monitor code coverage and report to coveralls.io

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New features:

* Generalized forward-backward splitting algorithm
* Projection-based primal-dual algorithm
* TV-norm function (eval, prox)
* Nuclear-norm function (eval, prox)
* L2-norm proximal operator supports non-tight frames
* Two new tutorials using the TV-norm with Forward-Backward and
Douglas-Rachford for image reconstruction and denoising
* New stopping criterion XTOL allows to stop when the variable is stable

Bug fix:

* Much more memory efficient. Note that the array which contains the initial
solution is now modified in place.

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Bug fix version. Still experimental.

Bug fixes:

* Avoid complex casting to real
* Do not stop iterating if the objective function stays at zero

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Second usable version, available on GitHub and released on PyPI.
Still experimental.

New features:

* Douglas-Rachford splitting algorithm
* Projection on the L2-ball for tight and non tight frames
* Compressed sensing tutorial using L2-ball, L2-norm and Douglas-Rachford
* Automatic solver selection

Infrastructure:

* Unit tests for all functions and solvers
* Continuous integration testing on Python 2.6, 2.7, 3.2, 3.3 and 3.4