  In this paper, we extend the previous convergence results for the generalized
alternating projection method applied to subspaces in [arXiv:1703.10547] to
hold also for smooth manifolds. We show that the algorithm locally behaves
similarly in the subspace and manifold settings and that the same rates are
obtained. We also present convergence rate results for when the algorithm is
applied to non-empty, closed, and convex sets. The results are based on a
finite identification property that implies that the algorithm after an initial
identification phase solves a smooth manifold feasibility problem. Therefore,
the rates in this paper hold asymptotically for problems in which this
identification property is satisfied. We present a few examples where this is
the case and also a counter example for when this is not.
