Projection, Degeneracy, and Singularity Degree for Spectrahedra

Facial reduction, FR, is a regularization technique for convex programs where the strict feasibility constraint qualification, CQ, this http URL this CQ holds generically, failure is pervasive in applications such as semidefinite relaxations of hard discrete optimization problems.

In this paper we relate FR to the analysis of the convergence behaviour of a semismooth Newton root finding method for the projection onto a spectrahedron, i.e., onto the intersection of a linear manifold and the semidefinite cone.

We examine the effect of failure of strict feasibility on the projection problem.

In the process, we derive an elegant formula for the projection onto a face of the semidefinite cone obtained via regularization and discuss pathologies that arise in the absence of strict feasibility.

We show further that the ill-conditioning of the Jacobian of the Newton method near optimality characterizes the degeneracy of the nearest point in the spectrahedron.

We apply the results, both theoretically and empirically, to the problem of finding nearest points to the sets of: (i) correlation matrices or the elliptope; and (ii) semidefinite relaxations of permutation matrices or the vontope, i.e., the feasible sets for the semidefinite relaxations of the max-cut and quadratic assignment problems, respectively.