We propose to apply artificial intelligence approaches in a warm-starting procedure to
accelerate active set methods that are used to solve strictly convex quadratic programs in …

Our quality of life, the world's productivity and its sustainability become more and more
determined by the outcome and benefits of process automation. In this domain of automatic …

With model predictive control (MPC) becoming a viable approach for advanced feedback
control at very fast sampling times, a plethora of methods for solving quadratic programming …

The goal of this thesis is to investigate algorithms and methods to reduce the solution time of
solvers for Model Predictive Control (MPC). The thesis is accompanied with an open-source …

Deep neural networks have revolutionized many fields, including image processing, inverse
problems, text mining and more recently, give very promising results in systems and control …

We propose a Newton-type alternating minimization algorithm (NAMA) for solving structured
nonsmooth convex optimization problems where the sum of two functions is to be minimized …

This work presents an algorithmic scheme for solving the infinite-time constrained linear
quadratic regulation problem. We employ an accelerated version of a popular proximal …

It is well-known that primal first-order algorithms achieve sublinear (linear) convergence for
smooth convex (smooth strongly convex) constrained minimization. However, these …

Algorithms and Methods for Fast Model Predictive Control Page 1 Algorithms and Methods
for Fast Model Predictive Control Gianluca Frison Technical University of Denmark …

In this paper we present the toolbox DuQuad specialized for solving general convex
quadratic problems arising in many engineering applications (eg embedded predictive …