Convergence of proximal gradient methods

First order methods have recently been widely applied to solve convex optimization problems in a
variety of areas including machine learning and signal processing.
In particular, proximal gradient algorithms (a.k.a. forward-backward splitting algorithms) and their
accelerated variants have received considerable attention. These algorithms are easy to implement
and suitable for solving high dimensional problems thanks to the low memory requirement of each iteration.
In this talk I will present some recent convergence results for this class of methods.