Inexact Proximal Gradient Methods for Non-convex and Non-smooth Optimization

Understand

In machine learning research, the proximal gradient methods are popular for solving various optimization problems with non-smooth regularization.

• Inexact proximal gradient methods are extremely important when exactly solving the proximal operator is time-consuming, or the proximal operator does not have an analytic solution.
• However, existing inexact proximal gradient methods only consider convex problems.
• The knowledge of inexact proximal gradient methods in the non-convex setting is very limited.