Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems

Understand

The proximal gradient algorithm has been popularly used for convex optimization.

• Recently, it has also been extended for nonconvex problems, and the current state-of-the-art is the nonmonotone accelerated proximal gradient algorithm.
• However, it typically requires two exact proximal steps in each iteration, and can be inefficient when the proximal step is expensive.
• In this paper, we propose an efficient proximal gradient algorithm that requires only one inexact (and thus less expensive) proximal step in each iteration.