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The classic Alternating Direction Method of Multipliers (ADMM) is a popularframework to solve linear-equality constrained problems. In this paper, weextend the ADMM naturally to nonlinear equality-constrained problems, calledneADMM. The difficulty of neADMM is to solve nonconvex subproblems. We provideglobally optimal solutions to them in two important applications. Experimentson synthetic and real-world datasets demonstrate excellent performance andscalability of our proposed neADMM over existing state-of-the-start methods.

Nonconvex Generalization of Alternating Direction Method of Multipliers for Nonlinear Equality Constrained Problems