  A novel splitting scheme to solve parametric multiconvex programs is
presented. It consists of a fixed number of proximal alternating minimisations
and a dual update per time step, which makes it attractive in a real-time
Nonlinear Model Predictive Control (NMPC) framework and for distributed
computing environments. Assuming that the parametric program is semi-algebraic
and that its KKT points are strongly regular, a contraction estimate is derived
and it is proven that the sub-optimality error remains stable if two key
parameters are tuned properly. Efficacy of the method is demonstrated by
solving a bilinear NMPC problem to control a DC motor.
