Development status updated from Alpha to Beta.
- Acceleration module, decoupling acceleration strategies from the solvers
- Backtracking scheme
- FISTA acceleration
- 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