Starting in the late 1970s, optimization-based control has built up an impressive track record
of successful industrial applications, in particular in the petrochemical and process …

This paper presents the acados software package, a collection of solvers for fast embedded
optimization intended for fast embedded applications. Its interfaces to higher-level …

This article presents a sparse, low-memory footprint optimization algorithm for the
implementation of model predictive control (MPC) for tracking formulation in embedded …

We study connections between the alternating direction method of multipliers (ADMM), the
classical method of multipliers (MM), and progressive hedging (PH). The connections are …

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 novel feasible-path algorithm to solve the optimal power flow (OPF) problem
for real-time use cases. The method augments the seminal work of Dommel and Tinney with …

Torque distribution optimization of electric vehicles should consider various demands,
including energy conservation, power, and tire antislip, which are usually coupled …

The main objective of this study is to develop primal–dual differential dynamic programming
(DDP), a model-based reinforcement learning (RL) framework that can handle constrained …

In this thesis, we discuss several techniques for solving nonlinear optimization problems
arising in nonlinear model predictive control (NMPC). They share two things in common …

We study the solution of block-structured linear algebra systems arising in optimization by
using iterative solution techniques. These systems are the core computational bottleneck of …