Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity

In this work we bridge these two approaches under a strict complementarity assumption, which in particular implies that the optimal solution to the convex relaxation is unique and is also the optimal solution to the original nonconvex problem.