Bregman ADMM provably avoids saddle points and finds second-order stationary solutions for nonconvex problems without Lipschitz gradient requirements, making it applicable to polynomial and tensor optimization problems where standard methods fail.
This paper analyzes Bregman ADMM, an optimization algorithm for nonconvex problems with linear constraints that don't require standard smoothness assumptions.