14 papers
Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models
Manuel Berkemeier, Sebastian Peitz
We present a flexible trust region descend algorithm for unconstrained and convexly constrained multiobjective optimization problems. It is targeted at heterogeneous and expensive…
On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation
Katharina Bieker, Bennet Gebken, Sebastian Peitz
We present a novel algorithm that allows us to gain detailed insight into the effects of sparsity in linear and nonlinear optimization, which is of great importance in many scienti…
An efficient descent method for locally Lipschitz multiobjective optimization problems
Bennet Gebken, Sebastian Peitz
In this article, we present an efficient descent method for locally Lipschitz continuous multiobjective optimization problems (MOPs). The method is realized by combining a theoreti…
Data-Driven Model Predictive Control using Interpolated Koopman Generators
Sebastian Peitz, Samuel E. Otto, Clarence W. Rowley
In recent years, the success of the Koopman operator in dynamical systems analysis has also fueled the development of Koopman operator-based control frameworks. In order to preserv…
Explicit Multi-objective Model Predictive Control for Nonlinear Systems Under Uncertainty
Carlos Ignacio Hernández Castellanos, Sina Ober-Blöbaum, Sebastian Peitz
In real-world problems, uncertainties (e.g., errors in the measurement, precision errors) often lead to poor performance of numerical algorithms when not explicitly taken into acco…
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
Stefan Klus, Feliks Nüske, Sebastian Peitz +3
We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode deco…