  This paper considers optimization problems on Riemannian manifolds and
analyzes iteration-complexity for gradient and subgradient methods on manifolds
with non-negative curvature. By using tools from the Riemannian convex analysis
and exploring directly the tangent space of the manifold, we obtain different
iteration-complexity bounds for the aforementioned methods, complementing and
improving related results. Moreover, we also establish iteration-complexity
bound for the proximal point method on Hadamard manifolds.
