  In this paper we analyze a class of nonconvex optimization problem from the
viewpoint of abstract convexity. Using the respective generalizations of the
subgradient we propose an abstract notion proximal operator and derive a number
of algorithms, namely an abstract proximal point method, an abstract
forward-backward method and an abstract projected subgradient method. Global
convergence results for all algorithms are discussed and numerical examples are
given
