Restricted Normal Cones and Sparsity Optimization with Affine Constraints

The problem of finding a vector with the fewest nonzero elements that satisfies an underdetermined system of linear equations is an NP-complete problem that is typically solved numerically via convex heuristics or nicely-behaved nonconvex relaxations. In this paper we consider the elementary method of alternating projections (MAP) for solving the sparsity optimization problem without employing convex heuristics. In a parallel paper we recently introduced the restricted normal cone which generalizes the classical Mordukhovich normal cone and reconciles some fundamental gaps in the theory of sufficient conditions for local linear convergence of the MAP algorithm. We use the restricted normal cone together with the notion of superregularity, which is inherently satisfied for the affine sparse optimization problem, to obtain local linear convergence results with estimates for the radius of convergence of the MAP algorithm applied to sparsity optimization with an affine constraint. Communicated by Emmanuel Candés Research of H.H.Bauschke was supported in part by the Natural Sciences and Engineering Research Council of Canada and by the Canada Research Chair Program. Research of D.R.Luke was supported in part by the German Research Foundation grant SFB755-A4. Research of H.M. Phan was supported in part by the Pacific Institute for the Mathematical Sciences and and by a University of British Columbia research grant. Research of X. Wang was supported in part by the Natural Sciences and Engineering Research Council of Canada. 2010 Mathematics Subject Classification: Primary 49J52, 49M20, 90C30; Secondary 15A29, 47H09, 65K05, 65K10, 94A08.