Pu, YeZeilinger, Melanie N.Jones, Colin N.2017-03-272017-03-272017-03-27201710.1109/Tac.2016.2561407https://infoscience.epfl.ch/handle/20.500.14299/135899WOS:000395510600029In this technical note, the fast alternating minimization algorithm (FAMA) is proposed to solve model predictive control (MPC) problems with polytopic and second-order cone constraints. Two splitting strategies with efficient implementations for MPC problems are presented. We derive computational complexity certificates for both splitting strategies, by providing complexity upper-bounds on the number of iterations required to provide a certain accuracy of the dual function value and, most importantly, of the primal solution. This is of particular relevance in the context of real-time MPC in order to bound the required on-line computation time. We further address the computation of the complexity bounds, requiring the solution of a non-convex minimization problem. Finally, we demonstrate the performance of FAMA compared to other splitting methods using a quadrotor example.Complexity upper-boundfast alternation minimization algorithm (FAMA)model predictive controlsecond-order cone constraintssplitting methodstext::journal::journal article::research article