  We investigate two greedy strategies for finding an approximation to the
minimum of a convex function $E$ defined on a Hilbert space $H$. We prove
convergence rates for these algorithms under suitable conditions on the
objective function $E$. These conditions involve the behavior of the modulus of
smoothness and the modulus of uniform convexity of $E$.
