- Monotone+Lipschitz Forward-Backward-Forward primal-dual algorithm
- Projection-based primal-dual algorithm
- L2-norm proximal operator supports non-tight frames
Bug fixes :
- prox_tv_2d has been fixed
- Continuous integration testing on Python 2.7, 3.3, 3.4 and 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
New feature version. Still experimental.
- norm_tv has been added with gradient, div, evaluation and prox.
- Module signals has been added.
- A demo for douglas rachford is also now present.
Bug fix version. Still experimental.
Bug fixes :
- 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.
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
- 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)
- TV-norm function (eval, grad, div 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