Abstract We study Model Predictive Control (MPC) and propose a general analysis pipeline
to bound its dynamic regret. The pipeline first requires deriving a perturbation bound for a …

We study solution sensitivity for nonlinear programs (NLPs) whose structures are induced by
graphs. These NLPs arise in many applications such as dynamic optimization, stochastic …

We study a real-time iteration (RTI) scheme for solving online optimization problem
appeared in nonlinear optimal control. The proposed RTI scheme modifies the existing RTI …

A classical approach for solving discrete time nonlinear control on a finite horizon consists in
repeatedly minimizing linear quadratic approximations of the original problem around …

We study the convergence properties of an overlapping Schwarz decomposition algorithm
for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time …

We propose a fast temporal decomposition procedure for solving long-horizon nonlinear
dynamic programs. The core of the procedure is sequential quadratic programming (SQP) …

We study a property of dynamic optimization (DO) problems (as those encountered in model
predictive control and moving horizon estimation) that is known as exponential decay of …

The Minimum Lap Time Problem (MLTP) remains a significant area of research, particularly
in the motorsport context. This form of Optimal Control Problem (OCP) aims to minimise lap …

We study the problem of Trajectory Optimization (TO) for a general class of stiff and
constrained dynamic systems. We establish a set of mild assumptions, under which we show …

In this paper, we address the problem of solving large-scale graph-structured nonlinear
programs (gsNLPs) in a scalable manner. GsNLPs are problems in which the objective and …