Second-Order KKT Guarantees for Bregman ADMM in Nonconvex and Non-Lipschitz Optimization

We analyze Bregman ADMM for nonconvex linearly constrained problems under two-sided relative smoothness, a condition that replaces the standard Lipschitz gradient assumption with a Hessian comparison relative to a Bregman kernel.

This setting covers polynomial objectives arising in matrix and tensor models for which a global Lipschitz-gradient constant need not exist.

We show that on an invariant open state-space domain, one iteration of Bregman ADMM defines a smooth primal--dual fixed-point map whose strict-saddle KKT points are unstable fixed points; consequently, from random initialization the iterates converge to a strict saddle with probability zero.

Combined with existing first-order convergence results, this yields almost-sure second-order stationarity of limiting KKT points.

We extend the analysis to a multi-block star consensus formulation for distributed optimization.

The technical novelty lies in a determinant reduction with a Bregman-specific symmetrization and scaling step in the two block spectral argument, together with a null space cancellation exploiting the star graph structure in the consensus case.

Numerical experiments on distributed matrix factorization illustrate the theory, and a symmetric tensor factorization example demonstrates the broader Bregman proximal splitting idea beyond the separable consensus setting.