  The classic Alternating Direction Method of Multipliers (ADMM) is a popular
framework to solve linear-equality constrained problems. In this paper, we
extend the ADMM naturally to nonlinear equality-constrained problems, called
neADMM. The difficulty of neADMM is to solve nonconvex subproblems. We provide
globally optimal solutions to them in two important applications. Experiments
on synthetic and real-world datasets demonstrate excellent performance and
scalability of our proposed neADMM over existing state-of-the-start methods.
