A Python convex optimization package using proximal splitting methods
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PyUNLocBoX: Optimization by Proximal Splitting

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The PyUNLocBoX is a Python package which uses proximal splitting methods to solve non-differentiable convex optimization problems. The documentation is available on Read the Docs and development takes place on GitHub. A (mostly unmaintained) Matlab version exists.

The package is designed to be easy to use while allowing any advanced tasks. It is not meant to be a black-box optimization tool. You'll have to carefully design your solver. In exchange you'll get full control of what the package does for you, without the pain of rewriting the proximity operators and the solvers and with the added benefit of tested algorithms. With this package, you can focus on your problem and the best way to solve it rather that the details of the algorithms. It comes with the following solvers:

  • Gradient descent
  • Forward-backward splitting algorithm (FISTA, ISTA)
  • Douglas-Rachford splitting algorithm
  • Generalized forward-backward
  • Monotone+Lipschitz forward-backward-forward primal-dual algorithm
  • Projection-based primal-dual algorithm

Moreover, the following acceleration schemes are included:

  • FISTA acceleration scheme
  • Backtracking based on a quadratic approximation of the objective
  • Regularized nonlinear acceleration (RNA)

To compose your objective, you can either define your custom functions (which should implement an evaluation method and a gradient or proximity method) or use one of the followings:

  • L1-norm
  • L2-norm
  • TV-norm
  • Nuclear-norm
  • Projection on the L2-ball

Following is a typical usage example who solves an optimization problem composed by the sum of two convex functions. The functions and solver objects are first instantiated with the desired parameters. The problem is then solved by a call to the solving function.

>>> from pyunlocbox import functions, solvers
>>> f1 = functions.norm_l2(y=[4, 5, 6, 7])
>>> f2 = functions.dummy()
>>> solver = solvers.forward_backward()
>>> ret = solvers.solve([f1, f2], [0., 0, 0, 0], solver, atol=1e-5)
Solution found after 9 iterations:
    objective function f(sol) = 6.714385e-08
    stopping criterion: ATOL
>>> ret['sol']
array([3.99990766, 4.99988458, 5.99986149, 6.99983841])

You can try it online, look at the tutorials to learn how to use it, or look at the reference guide for an exhaustive documentation of the API. Enjoy!


The PyUNLocBoX is available on PyPI:

$ pip install pyunlocbox

The PyUNLocBoX is available on conda-forge:

$ conda install -c conda-forge pyunlocbox


See the guidelines for contributing in CONTRIBUTING.rst.


The PyUNLocBoX was started in 2014 as an academic open-source project for research purpose at the EPFL LTS2 laboratory.

The code in this repository is released under the terms of the BSD 3-Clause license.

If you are using the library for your research, for the sake of reproducibility, please cite the version you used as indexed by Zenodo. Or cite the generic concept as:

  title = {PyUNLocBoX: Optimization by Proximal Splitting},
  author = {Defferrard, Micha\"el and Pena, Rodrigo and Perraudin, Nathana\"el},
  doi = {10.5281/zenodo.1199081},
  url = {https://github.com/epfl-lts2/pyunlocbox/},