  Decentralized non-convex optimization is important in many problems of
practical relevance. Existing decentralized methods, however, typically either
lack convergence guarantees for general non-convex problems, or they suffer
from a high subproblem complexity. We present a novel bi-level SQP method,
where the inner quadratic problems are solved via ADMM. A decentralized
stopping criterion from inexact Newton methods allows the early termination of
ADMM as an inner algorithm to improve computational efficiency. The method has
local convergence guarantees for non-convex problems. Moreover, it only solves
sequences of Quadratic Programs, whereas many existing algorithms solve
sequences of Nonlinear Programs. The method shows competitive numerical
performance for an optimal power flow problem.
