TY - JOUR
AU - Frühbis-Krüger, Anne
AU - Liebendörfer, Michael
ID - 8571
IS - 63
JF - Computeralgebra-Rundbrief
TI - Minisymposium CAS in der Hochschullehre - ein Blick in die Praxis
ER -
TY - JOUR
AU - Jin, Ligang
AU - Mazzuoccolo, Giuseppe
AU - Steffen, Eckhard
ID - 10132
JF - Discussiones Mathematicae Graph Theory
SN - 1234-3099
TI - Cores, joins and the Fano-flow conjectures
VL - 38
ER -
TY - CHAP
AB - In this chapter, we combine a global, derivative-free subdivision algorithm for multiobjective optimization problems with a posteriori error estimates for reduced-order models based on Proper Orthogonal Decomposition in order to efficiently solve multiobjective optimization problems governed by partial differential equations. An error bound for a semilinear heat equation is developed in such a way that the errors in the conflicting objectives can be estimated individually. The resulting algorithm constructs a library of locally valid reduced-order models online using a Greedy (worst-first) search. Using this approach, the number of evaluations of the full-order model can be reduced by a factor of more than 1000.
AU - Beermann, Dennis
AU - Dellnitz, Michael
AU - Peitz, Sebastian
AU - Volkwein, Stefan
ID - 8754
SN - 9783319753188
T2 - Reduced-Order Modeling (ROM) for Simulation and Optimization
TI - Set-Oriented Multiobjective Optimal Control of PDEs Using Proper Orthogonal Decomposition
ER -
TY - GEN
AB - Kernel transfer operators, which can be regarded as approximations of
transfer operators such as the Perron-Frobenius or Koopman operator in
reproducing kernel Hilbert spaces, are defined in terms of covariance and
cross-covariance operators and have been shown to be closely related to the
conditional mean embedding framework developed by the machine learning
community. The goal of this paper is to show how the dominant eigenfunctions of
these operators in combination with gradient-based optimization techniques can
be used to detect long-lived coherent patterns in high-dimensional time-series
data. The results will be illustrated using video data and a fluid flow
example.
AU - Klus, Stefan
AU - Peitz, Sebastian
AU - Schuster, Ingmar
ID - 16293
T2 - arXiv:1805.10118
TI - Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions
ER -
TY - CHAP
AU - Frühbis-Krüger, Anne
AU - Kemper, Gregor
AU - Koepf, Wolfram
AU - Liebendörfer, Michael
ID - 8572
T2 - Beiträge zum Mathematikunterricht 2018
TI - CAS in der Hochschullehre - Ein Blick in die Praxis
ER -
TY - JOUR
AU - Jurgelucks, Benjamin
AU - Claes, Leander
AU - Walther, Andrea
AU - Henning, Bernd
ID - 6571
JF - Optimization Methods and Software
SN - 1055-6788
TI - Optimization of triple-ring electrodes on piezoceramic transducers using algorithmic differentiation
ER -
TY - GEN
AU - Feldmann, Nadine
AU - Jurgelucks, Benjamin
AU - Claes, Leander
AU - Henning, Bernd
ID - 6595
T2 - Messtechnische Anwendungen von Ultraschall
TI - Vollständige Charakterisierung von piezoelektrischen Scheiben mit Ringelektroden
ER -
TY - JOUR
AB - Dynamic mode decomposition (DMD) is a recently developed tool for the analysis of the behavior of complex dynamical systems. In this paper, we will propose an extension of DMD that exploits low-rank tensor decompositions of potentially high-dimensional data sets to compute the corresponding DMD modes and eigenvalues. The goal is to reduce the computational complexity and also the amount of memory required to store the data in order to mitigate the curse of dimensionality. The efficiency of these tensor-based methods will be illustrated with the aid of several different fluid dynamics problems such as the von Kármán vortex street and the simulation of two merging vortices.
AU - Klus, Stefan
AU - Gelß, Patrick
AU - Peitz, Sebastian
AU - Schütte, Christof
ID - 8755
IS - 7
JF - Nonlinearity
SN - 0951-7715
TI - Tensor-based dynamic mode decomposition
VL - 31
ER -
TY - GEN
AB - Model predictive control is a prominent approach to construct a feedback
control loop for dynamical systems. Due to real-time constraints, the major
challenge in MPC is to solve model-based optimal control problems in a very
short amount of time. For linear-quadratic problems, Bemporad et al.~have
proposed an explicit formulation where the underlying optimization problems are
solved a priori in an offline phase. In this article, we present an extension
of this concept in two significant ways. We consider nonlinear problems and --
more importantly -- problems with multiple conflicting objective functions. In
the offline phase, we build a library of Pareto optimal solutions from which we
then obtain a valid compromise solution in the online phase according to a
decision maker's preference. Since the standard multi-parametric programming
approach is no longer valid in this situation, we instead use interpolation
between different entries of the library. To reduce the number of problems that
have to be solved in the offline phase, we exploit symmetries in the dynamical
system and the corresponding multiobjective optimal control problem. The
results are verified using two different examples from autonomous driving.
AU - Ober-Blöbaum, Sina
AU - Peitz, Sebastian
ID - 16294
T2 - arXiv:1809.06238
TI - Explicit multiobjective model predictive control for nonlinear systems with symmetries
ER -
TY - CONF
AB - In this article we propose a descent method for equality and inequality constrained multiobjective optimization problems (MOPs) which generalizes the steepest descent method for unconstrained MOPs by Fliege and Svaiter to constrained problems by using two active set strategies. Under some regularity assumptions on the problem, we show that accumulation points of our descent method satisfy a necessary condition for local Pareto optimality. Finally, we show the typical behavior of our method in a numerical example.
AU - Gebken, Bennet
AU - Peitz, Sebastian
AU - Dellnitz, Michael
ID - 8750
SN - 1860-949X
T2 - Numerical and Evolutionary Optimization – NEO 2017
TI - A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems
ER -