  A novel augmented Lagrangian method for solving non-convex programs with
nonlinear cost and constraint couplings in a distributed framework is
presented. The proposed decomposition algorithm is made of two layers: The
outer level is a standard multiplier method with penalty on the nonlinear
equality constraints, while the inner level consists of a block-coordinate
descent (BCD) scheme. Based on standard results on multiplier methods and
recent results on proximal regularised BCD techniques, it is proven that the
method converges to a KKT point of the non-convex nonlinear program under a
semi-algebraicity assumption. Efficacy of the algorithm is demonstrated on a
numerical example.
