Semismooth Newton methods for degenerate polyhedral projection

In this paper, we study dual semismooth Newton (SSN) methods for degenerate polyhedral projection problems, where generalized Jacobians of the dual residual may remain singular even arbitrarily close to the solution set.

Rather than regularizing these singular systems, we exploit the nonuniqueness of the dual representation.

We introduce a primal--dual lifted projection-equivalent set that always possesses extreme points without additional structural assumptions on the polyhedron, and show that its extreme-point geometry identifies dual representatives at which nonsingular generalized Jacobians of the dual residual can be constructed.

This geometry is further linked to a full-column-rank condition and a generalized weak strict Robinson constraint qualification, showing that the regularity required by the Newton step can be recovered rather than imposed \emph{a priori}.

We also establish displacement bounds that connect representative selection throughout the algorithm with the local Newton mechanism.

Building on this variational framework, we develop an inexact dual SSN method with local superlinear convergence and a globalized version combining monotone representative selection with a Wolfe line search.

The resulting method is globally convergent and eventually recovers the fast local rate.

Numerical experiments on regularized optimal transport, battery-scheduling feasibility restoration, and occupation-measure projection demonstrate its robustness in highly degenerate settings.