  The proximal point algorithm, which is a well-known tool for finding minima
of convex functions, is generalized from the classical Hilbert space framework
into a nonlinear setting, namely, geodesic metric spaces of nonpositive
curvature. We prove that the sequence generated by the proximal point algorithm
weakly converges to a minimizer, and also discuss a related question:
convergence of the gradient flow.
