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.
Development status updated from Alpha to Beta.
Acceleration module, decoupling acceleration strategies from the solvers
FISTA with backtracking
Regularized non-linear acceleration (RNA)
Solvers: gradient descent algorithm
Decrease dimensionality of variables in Douglas Rachford tutorial to reduce test time and timeout on Travis CI.
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.
Monotone+Lipschitz forward-backward-forward primal-dual algorithm (MLFBF)
Plots generated when building documentation (not stored in the repository)
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
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
Much more memory efficient. Note that the array which contains the initial solution is now modified in place.
Bug fix version. Still experimental.
Avoid complex casting to real
Do not stop iterating if the objective function stays at zero
Second usable version, available on GitHub and released on PyPI. Still experimental.
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
Unit tests for all functions and solvers
Continuous integration testing on Python 2.6, 2.7, 3.2, 3.3 and 3.4
First usable version, available on GitHub and released on PyPI. Still experimental.
Forward-backward splitting algorithm
L1-norm function (eval and prox)
L2-norm function (eval, grad and prox)
Least square problem tutorial using L2-norm and forward-backward
Compressed sensing tutorial using L1-norm, L2-norm and forward-backward
Sphinx generated documentation using Numpy style docstrings
Documentation hosted on Read the Docs
Code hosted on GitHub
Package hosted on PyPI
Code checked by flake8
Docstring and tutorial examples checked by doctest (as a test suite)
Unit tests for functions module (as a test suite)
All test suites executed in Python 2.6, 2.7 and 3.2 virtualenvs by tox
Distributed automatic testing on Travis CI continuous integration platform