@inbook{8573,
  author       = {{Liebendörfer, Michael}},
  booktitle    = {{Beiträge zum Mathematikunterricht 2018}},
  editor       = {{Didaktik der Mathematik der Universität Paderborn, Fachgruppe}},
  pages        = {{1171--1174}},
  publisher    = {{WTM-Verlag}},
  title        = {{{Psychologische Grundbedürfnisse im frühen Mathematikstudium}}},
  year         = {{2018}},
}

@inbook{8574,
  author       = {{Liebendörfer, Michael and Kuklinski, Christiane and Hochmuth, Reinhard}},
  booktitle    = {{Beiträge zum Mathematikunterricht 2018}},
  editor       = {{Didaktik der Mathematik der Universität Paderborn, Fachgruppe}},
  pages        = {{1175--1178}},
  publisher    = {{WTM-Verlag}},
  title        = {{{Auswirkungen von innovativen Vorlesungen für Lehramtsstudierende in der Studieneingangsphase}}},
  year         = {{2018}},
}

@book{8576,
  author       = {{Liebendörfer, Michael}},
  isbn         = {{978-3-658-22506-3 978-3-658-22507-0}},
  publisher    = {{Springer Fachmedien Wiesbaden}},
  title        = {{{Motivationsentwicklung im Mathematikstudium}}},
  doi          = {{10.1007/978-3-658-22507-0}},
  year         = {{2018}},
}

@inproceedings{8750,
  abstract     = {{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.}},
  author       = {{Gebken, Bennet and Peitz, Sebastian and Dellnitz, Michael}},
  booktitle    = {{Numerical and Evolutionary Optimization – NEO 2017}},
  isbn         = {{9783319961033}},
  issn         = {{1860-949X}},
  title        = {{{A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems}}},
  doi          = {{10.1007/978-3-319-96104-0_2}},
  year         = {{2018}},
}

@article{8751,
  abstract     = {{Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto-optimal solutions have led to a wide range of new applications related to optimal and feedback control, which results in new challenges such as expensive models or real-time applicability. Since the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging, which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview of recent developments in accelerating multiobjective optimal control for complex problems where either PDE constraints are present or where a feedback behavior has to be achieved. In the first case, surrogate models yield significant speed-ups. Besides classical meta-modeling techniques for multiobjective optimization, a promising alternative for control problems is to introduce a surrogate model for the system dynamics. In the case of real-time requirements, various promising model predictive control approaches have been proposed, using either fast online solvers or offline-online decomposition. We also briefly comment on dimension reduction in many-objective optimization problems as another technique for reducing the numerical effort.}},
  author       = {{Peitz, Sebastian and Dellnitz, Michael}},
  issn         = {{2297-8747}},
  journal      = {{Mathematical and Computational Applications}},
  number       = {{2}},
  title        = {{{A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction}}},
  doi          = {{10.3390/mca23020030}},
  volume       = {{23}},
  year         = {{2018}},
}

@inbook{8754,
  abstract     = {{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.}},
  author       = {{Beermann, Dennis and Dellnitz, Michael and Peitz, Sebastian and Volkwein, Stefan}},
  booktitle    = {{Reduced-Order Modeling (ROM) for Simulation and Optimization}},
  isbn         = {{9783319753188}},
  pages        = {{47--72}},
  title        = {{{Set-Oriented Multiobjective Optimal Control of PDEs Using Proper Orthogonal Decomposition}}},
  doi          = {{10.1007/978-3-319-75319-5_3}},
  year         = {{2018}},
}

@article{8755,
  abstract     = {{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.}},
  author       = {{Klus, Stefan and Gelß, Patrick and Peitz, Sebastian and Schütte, Christof}},
  issn         = {{0951-7715}},
  journal      = {{Nonlinearity}},
  number       = {{7}},
  pages        = {{3359--3380}},
  title        = {{{Tensor-based dynamic mode decomposition}}},
  doi          = {{10.1088/1361-6544/aabc8f}},
  volume       = {{31}},
  year         = {{2018}},
}

@inproceedings{8757,
  abstract     = {{A framework for set‐oriented multiobjective optimal control of partial differential equations using reduced order modeling has recently been developed [1]. Following concepts from localized reduced bases methods, error estimators for the reduced cost functionals are utilized to construct a library of locally valid reduced order models. This way, a superset of the Pareto set can efficiently be computed while maintaining a prescribed error bound. In this article, this algorithm is applied to a problem with non‐smooth objective functionals. Using an academic example, we show that the extension to non‐smooth problems can be realized in a straightforward manner. We then discuss the implications on the numerical results.}},
  author       = {{Beermann, Dennis and Dellnitz, Michael and Peitz, Sebastian and Volkwein, Stefan}},
  booktitle    = {{PAMM}},
  issn         = {{1617-7061}},
  pages        = {{51--54}},
  title        = {{{POD-based multiobjective optimal control of PDEs with non-smooth objectives}}},
  doi          = {{10.1002/pamm.201710015}},
  year         = {{2018}},
}

@misc{6595,
  author       = {{Feldmann, Nadine and Jurgelucks, Benjamin and Claes, Leander and Henning, Bernd}},
  title        = {{{Vollständige Charakterisierung von piezoelektrischen Scheiben mit Ringelektroden}}},
  year         = {{2018}},
}

@article{16713,
  author       = {{Gölz, Christian and Voelcker-Rehage, Claudia and Mora, Karin and Reuter, Eva-Maria and Godde, Ben and Dellnitz, Michael and Reinsberger, Claus and Vieluf, Solveig}},
  issn         = {{1664-042X}},
  journal      = {{Frontiers in Physiology}},
  title        = {{{Improved Neural Control of Movements Manifests in Expertise-Related Differences in Force Output and Brain Network Dynamics}}},
  doi          = {{10.3389/fphys.2018.01540}},
  year         = {{2018}},
}

@article{16714,
  author       = {{Vieluf, Solveig and Mora, Karin and Gölz, Christian and Reuter, Eva-Maria and Godde, Ben and Dellnitz, Michael and Reinsberger, Claus and Voelcker-Rehage, Claudia}},
  issn         = {{0306-4522}},
  journal      = {{Neuroscience}},
  pages        = {{203--213}},
  title        = {{{Age- and Expertise-Related Differences of Sensorimotor Network Dynamics during Force Control}}},
  doi          = {{10.1016/j.neuroscience.2018.07.025}},
  year         = {{2018}},
}

@article{16715,
  author       = {{Bittracher, Andreas and Koltai, Péter and Klus, Stefan and Banisch, Ralf and Dellnitz, Michael and Schütte, Christof}},
  issn         = {{0938-8974}},
  journal      = {{Journal of Nonlinear Science}},
  pages        = {{471--512}},
  title        = {{{Transition Manifolds of Complex Metastable Systems}}},
  doi          = {{10.1007/s00332-017-9415-0}},
  volume       = {{28}},
  year         = {{2018}},
}

@unpublished{16292,
  abstract     = {{In a recent article, we presented a framework to control nonlinear partial
differential equations (PDEs) by means of Koopman operator based reduced models
and concepts from switched systems. The main idea was to transform a control
system into a set of autonomous systems for which the optimal switching
sequence has to be computed. These individual systems can be approximated very
efficiently by reduced order models obtained from data, and one can guarantee
equality of the full and the reduced objective function under certain
assumptions. In this article, we extend these results to continuous control
inputs using convex combinations of multiple Koopman operators corresponding to
constant controls, which results in a bilinear control system. Although
equality of the objectives can be carried over when the PDE depends linearly on
the control, we show that this approach is also valid in other scenarios using
several flow control examples of varying complexity.}},
  author       = {{Peitz, Sebastian}},
  booktitle    = {{arXiv:1801.06419}},
  title        = {{{Controlling nonlinear PDEs using low-dimensional bilinear approximations  obtained from data}}},
  year         = {{2018}},
}

@unpublished{16293,
  abstract     = {{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.}},
  author       = {{Klus, Stefan and Peitz, Sebastian and Schuster, Ingmar}},
  booktitle    = {{arXiv:1805.10118}},
  title        = {{{Analyzing high-dimensional time-series data using kernel transfer  operator eigenfunctions}}},
  year         = {{2018}},
}

@article{10129,
  abstract     = {{There are many hard conjectures in graph theory, like Tutte's 5-flow conjecture, and the 5-cycle double cover conjecture, which would be true in general if they would be true for cubic graphs. Since most of them are trivially true for 3-edge-colorable cubic graphs, cubic graphs which are not 3-edge-colorable, often called snarks, play a key role in this context. Here, we survey parameters measuring how far apart a non 3-edge-colorable graph is from being 3-edge-colorable. We study their interrelation and prove some new results. Besides getting new insight into the structure of snarks, we show that such  measures give partial results with respect to these important conjectures. The paper closes with a list of open problems and conjectures.}},
  author       = {{Fiol, M. A. and Mazzuoccolo, Guiseppe and Steffen, Eckhard}},
  journal      = {{The Electronic Journal of Combinatorics}},
  keywords     = {{Cubic graph, Tait coloring, Snark, Boole coloring, Berge's conjecture, Tutte's 5-flow conjecture}},
  number       = {{4}},
  title        = {{{Measures of Edge-Uncolorability of Cubic Graphs}}},
  volume       = {{25}},
  year         = {{2018}},
}

@article{10132,
  author       = {{Jin, Ligang and Mazzuoccolo, Giuseppe and Steffen, Eckhard}},
  issn         = {{1234-3099}},
  journal      = {{Discussiones Mathematicae Graph Theory}},
  pages        = {{165--175}},
  title        = {{{Cores, joins  and the Fano-flow conjectures}}},
  doi          = {{10.7151/dmgt.1999}},
  volume       = {{38}},
  year         = {{2018}},
}

@article{10142,
  author       = {{Steffen, Eckhard}},
  journal      = {{Australasian Journal of Combinatorics}},
  number       = {{1}},
  pages        = {{153--160}},
  title        = {{{Approximating Vizing’s independence number conjecture}}},
  volume       = {{71}},
  year         = {{2018}},
}

@inproceedings{31946,
  author       = {{Nührenbörger, M. and Bönig, D. and Häsel-Weide, Uta and Korff, N. and Scherer, P.}},
  booktitle    = {{Beiträge zum Mathematikunterricht 2018. Vorträge zur Mathematikdidaktik und zur Schnittstelle Mathematik/Mathematikdidaktik auf der gemeinsamen Jahrestagung der GDM und DMV}},
  editor       = {{In:, Fachgruppe Didaktik der Mathematik der Universität Paderborn}},
  pages        = {{103--104}},
  title        = {{{Inklusiver Mathematikunterricht - vernetzt zwischen Mathematikdidaktik und Sonderpädagogik}}},
  year         = {{2018}},
}

@article{31953,
  author       = {{Moser-Opitz, E. and Grob, U. and Wittich, C. and Häsel-Weide, Uta and Nührenbörger, M.}},
  journal      = {{Learning Disabilities: A Contemporary Journal}},
  number       = {{16}},
  pages        = {{19--35}},
  title        = {{{Fostering the Computation Competence of Low Achievers through Cooperative Learning in Inclusive Classrooms: A Longitudinal Study}}},
  year         = {{2018}},
}

@misc{31952,
  author       = {{Häsel-Weide, Uta and Nührenbörger, M. and Reinold, M.}},
  isbn         = {{978-3122009977}},
  pages        = {{144}},
  publisher    = {{Klett}},
  title        = {{{Förderkommentar Lernen zum Zahlenbuch 3}}},
  year         = {{2018}},
}

