Proximal Point Type Algorithms with Relaxed and Inertial Effects Beyond Convexity
Résumé
We show that the recent relaxed-inertial proximal point algorithm due to Attouch and Cabot remains convergent when the function to be minimized
is not convex, being only endowed with certain generalized convexity properties. Numerical experiments showcase the improvements brought by
the relaxation and inertia features to the standard proximal point method in this setting, too.