In the paper, we present algorithms for minimization of d.c. functions (difference of two convex functions) on the whole space$$R^n$$. Many nonconvex optimization problems can be described using these functions. D.c. functions are used in various applications especially in optimization, but the problem to characterize them is not trivial, due to the fact that these functions are not differentiable and certainly are not convex. The class of these functions is contained in the class of quasidifferentiable functions. Proposed algorithms are based on known necessary optimality conditions and d.c. duality. Convergence to$$\inf $$ -stationary points is established under fairly general natural assumptions.