Nonconvex set intersection problems: From projection methods to the Newton method for super-regular sets

The problem of finding a point in the intersection of closed sets can be solved by the method of alternating projections and its variants. It was shown in earlier papers that for convex sets, the strategy of using quadratic programming (QP) to project onto the intersection of supporting halfspaces generated earlier by the projection process can lead to an algorithm that converges multiple-term superlinearly. The main contributions of this paper are to show that this strategy can be effective for super-regular sets, which are structured nonconvex sets introduced by Lewis, Luke and Malick. Manifolds should be approximated by hyperplanes rather than halfspaces. We prove the linear convergence of this strategy, followed by proving that superlinear and quadratic convergence can be obtained when the problem is similar to the setting of the Newton method. We also show an algorithm that converges at an arbitrarily fast linear rate if halfspaces from older iterations are used to construct the QP.