  We consider the problem of projecting a convex set onto a subspace, or
equivalently formulated, the problem of computing a set obtained by applying a
linear mapping to a convex feasible set. This includes the problem of
approximating convex sets by polyhedrons. The existing literature on convex
projections provides methods for bounded convex sets only, in this paper we
propose a method that can handle both bounded and unbounded problems. The
algorithms we propose build on the ideas of inner and outer approximation. In
particular, we adapt the recently proposed methods for solving unbounded convex
vector optimization problems to handle also the class of projection problems.
