  Convex optimization is an essential tool for machine learning, as many of its
problems can be formulated as minimization problems of specific objective
functions. While there is a large variety of algorithms available to solve
convex problems, we can argue that it becomes more and more important to focus
on efficient, scalable methods that can deal with big data. When the objective
function can be written as a sum of "simple" terms, proximal splitting methods
are a good choice. UNLocBoX is a MATLAB library that implements many of these
methods, designed to solve convex optimization problems of the form $\min_{x
\in \mathbb{R}^N} \sum_{n=1}^K f_n(x).$ It contains the most recent solvers
such as FISTA, Douglas-Rachford, SDMM as well a primal dual techniques such as
Chambolle-Pock and forward-backward-forward. It also includes an extensive list
of common proximal operators that can be combined, allowing for a quick
implementation of a large variety of convex problems.
