Abstract

We discuss a Bregman proximal point type algorithm for dealing with quasiconvex minimization. In particular, we prove that the Bregman proximal point type algorithm converges to a minimal point for the minimization problem of a certain class of quasiconvex functions without neither differentiability nor Lipschitz continuity assumptions, this class of nonconvex functions is known as strongly quasiconvex functions and, as a consequence, we revisited the general case of quasiconvex functions.