Hours, Jean-HubertJones, Colin2014-03-102014-03-102014-03-10201410.1109/ACC.2014.6858863https://infoscience.epfl.ch/handle/20.500.14299/101616WOS:000346492604146A 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.OptimisationNMPCtext::conference output::conference proceedings::conference paper