Otto-von-Guericke-Universität Magdeburg

 
 
 
 
 
 
 
 

Katja Mombaur

Improving human-centered robots by model-based optimization

Katja Mombaur
Katja Mombaur is a full professor at the Institute of Computer Engineering of Heidelberg University and head of the Optimization, Robotics & Biomechanics Chair, as well as coordinator of the Heidelberg Center for Motion Research. She holds a diploma degree in Aerospace Engineering from the University of Stuttgart and a Ph.D. degree in Mathematics from Heidelberg University and has worked as a researcher at Seoul National University and in LAAS-CNRS in Toulouse. In 2020, she will join the University of Waterloo as Canada Excellence Chair for Human-Centered Robotics & Machine Intelligence. Her research focuses on understanding human movement by a combined approach of model-based optimization and experiments and using this knowledge to improve motions of humanoid robots and the interactions of humans with exoskeletons, prostheses and external physical devices.

Time & Place
The presentation on November 26, 2019 will be given in building 02, room 210 at the Otto-von-Guericke-University of Magdeburg and starts at 5.00 p.m..

 
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MathCoRe Lecture

Detecting roles in very large graphs

Paul Van Dooren
Professor of Mathematical Engineering 
Université Catholique de Louvain, Belgium

Time & Place
The presentation on October 22nd, 2019 will be given in the Lukasklause (Schleinufer 1, 39104 Magdeburg) and starts at 5.00 p.m..

 
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MathCoRe Lecture

Coordination of Autonomous Vehicles at Traffic Junctions.
Theory and Experiments


Prof. Paolo Falcone
Department of Electrical Engineering
Chalmers University of Technology
Gothenburg, Sweden

 

Time & Place
The presentation on May 14, 2019 will be given in building 10, room 460 at the Otto-von-Guericke-University of Magdeburg and starts at 5.00 p.m..

Abstract
The next challenge, beyond high-level autonomous driving, is the coordination of autonomous vehicles, which is expected to fully enable the potential of autonomous driving technologies and heavily impact the society. Nevertheless, the safety and performance issues arising from the tight coupling between information losses and delays and the control system stability and performance must be accounted for at the design stage. Starting from a multi-vehicle coordination problem at traffic junctions, which has been experimentally demonstrated relying on both the IEEE 802.11p wireless standard and a 5G cellular network prototype, we will motivate a joint communication and control paradigm, where a central coordinator decides upon control inputs to a set of dynamical systems and  their access to the communication channel. We will show a few results from numerical examples and new research directions.

Short CV
Paolo Falcone is Associate Professor in the Mechatronics research group. His research focuses on constrained optimal control and verification methods, applied to autonomous and semi-autonomous mobile systems, cooperative driving and intelligent vehicles. He is involved in a range of projects, in cooperation with industry, focusing on autonomous driving, cooperative driving and vehicle dynamics control. His teaching subjects include Model predictive control, Vehicle dynamics control and Modeling and simulation of dynamical systems.
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MathCoRe Lecture

Adjoint-based optimization of a complete design chain in CFD

Prof. Dr. Andrea Walther
Institut für Mathematik
Universität Paderborn

 

Time & Place
The presentation on November 6, 2018 will be given in the Lukasklause (Schleinufer 1, 39104 Magdeburg) and starts at 5.00 p.m..

Abstract
The complete design chain in Computational Fluid Dynamics (CFD) covers the parameterization of the object to be optimized like, e.g., an air foil, the usage of a Computer Aided Design (CAD) tool to actually construct the air foil and a flow solver to compute the flow around the air foil. The optimization of such a complete design chain that includes a CAD tool is still a severe challenge. In this talk we present the technique of algorithmic differentiation (AD) to compute exact derivative information for a given simulation code. We discuss how AD can be applied to the CAD kernel within OpenCASCADE Technology and a suitable flow solver taking also the complexity of the derivative information into account. We will see that a gradient-based optimization using adjoint information is the only tractable way.  First numerical results for the optimization of a U-bend pipe used frequently as a cooling channel and of the TU Berlin stator as one example from turbo machinery are shown. This includes also a verification of the computed derivatives.
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MathCoRe Lecture

Global optimization of ODE constrained network problems on the example of gas transport

Prof. Dr. Marc Pfetsch
TU Darmstadt

Time & Place
The presentation on June, 5, 2018 will be given in the Lukasklause (Schleinufer 1, 39104 Magdeburg) and starts at 5.00 p.m. (Historischer Raum).

 Abstract
This talk considers a global optimization approach to solve mixed integer nonlinear optimization problems with ordinary differential equation constraints in network problems. We combine techniques from mixed-integer nonlinear programming with an adaptive discretization of differential equations within a spatial branch-and-bound framework. We show that certain discretization schemes allow to construct lower and upper convex relaxations for the ODE constraints, which are then used to construct linear relaxations. This approach does not need to introduce additional variables for the different discretization nodes. We will illustrate our approach on the example of stationary gas transport and will present computational results.
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Neue Seite

Optimization based planning and feedback control: an on-going journey

Gabriele Pannocchia
Associated Professor,
Department of Civil and Industrial Engineering,
University of Pisa

 

Time & Place
The presentation on March 19, 2018 will be given at the Otto-von-Guericke-University Magdeburg, Universitätsplatz 2, building 7 - room 208 and starts at 5 p.m..

Abstract
Optimization based strategies for planning and feedback control represent a general framework of numerical methods in which a (often deterministic) model of the system under consideration and its environment are exploited to achieve high-level goals (e.g., minimization of energy consumption, emission of pollutants, maximization of throughput, etc.) as well as more specific tasks (e.g. product quality control, robotic manipulation), while respecting a number of constraints arising from physical, safety or performance limits.

In this seminar, I review and analyze the main concepts, successes and ongoing challenges of optimization based methods, with a particular emphasis on how uncertainties can be dealt with effectively and efficiently using disturbance estimation techniques. During the seminar, I present several examples ranging from reaction processes to robotic systems.
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Optimal control, optimisation, market mechanisms and physics of smart energy systems

Prof. dr. ir. Jacquelien M.A. Scherpen
University of Groningen

 

Time & Place
The presentation on January 23, 2018 will be given in the Max Planck Institute Magdeburg, Großer Seminarraum 'Prigogine' and starts at 5.00 p.m..

 

Abstract
In this talk I will first give a combined distributed, hierarchical optimal control perspective using dual decomposition and pricing mechanisms for smart energy systems, and in particular for household prosumers (consumers and producers). In this setup a possible future (EU) market structure is taken into account. Furthermore, I will take a second perspective from the physics, where stabilisation is important, and optimisation is done on the welfare function. Some questions about the coupling of these two perspectives are raised.
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Arthur Krener

Minimum Energy Estimation and Moving Horizon Estimation



Prof. Dr. Arthur Krener
Naval Postgraduate School, Monterey

 

Time & Place
The presentation on November 6, 2017 will be given in the Senatssaal (G05) and starts at 5.00 p.m..

 

Abstract
Minimum Energy Estimation is a way of filtering the state of a nonlinear system from partial and inexact measurements. It is a generalization of Gauss' method of least squares.  Its application to filtering of control systems goes back at least to Mortenson who called it Maximum Likelyhood Estimation \cite{Mo68}.  For linear, Gaussian systems it reduces to maximum likelihood estimation (akaKalman Filtering) but this is not true for nonlinear systems. We prefer the name Minimum Energy Estimation (MEE) that was  introduced by Hijab \cite{Hi80}. Both Mortenson and Hijab  dealt with systems in continuous time, we extend their methods to discrete time systems and show how power series techniques can lessen the computational burden.

Moving Horizon Estimation (MHE) is a moving window version of MEE. It computes the solution to an optimal control problem over a past moving window that is constrained by the actual observations on the window.  The optimal state trajectory at the end of the window is the MEE estimate at this time. The cost in  the optimal control problem is usually taken to be an L2 norm of the three slack variables; the initial condion noise, the driving noise and the measurement noise. MHE requires the buffering of the measurements over the past window. The optimal control problem is solved in real time by a nonlinear program solver but it becomes more difficult as the length of the
window is increased.

The power series approach to MME can be applied to MHE and this permits the choice of a very short past window consisting of one time step.  This speeds up MHE and allows its real time implementaion on faster processes. We demonstrate its effective on the chaotic Lorenz attractor.
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Christoph Helmberg

Bundle Methods -- A Flexible Tool in Convex Relaxation and Optimization



Prof. Dr. Christoph Helmberg
TU Chemnitz

 

Time & Place
The presentation on October 24, 2017 will be given in the Festung Mark (oberes Gewölbe) and starts at 5.00 p.m..

 

Abstract
Solution approaches for large scale integer or stochastic optimization problems frequently employ Lagrangian relaxation or decomposition in order to break the problem into manageable pieces. Suitable multipliers are then determined by nonsmooth convex minimization algorithms. In this, subgradient algorithms are frequently employed because they are easy to implement and they have optimal convergence properties for first order oracles. Bundle methods try to make better use of the same information by collecting it over time.  While their convergence properties are hard to pin down, the choice of the cutting model and proximal term offers a lot of flexibility to adapt the method to ones needs. The choice of the proximal term allows to introduce scaling information. When dealing with sums of convex functions bundle methods open new possibilities for asynchronous parallel optimization approaches.  In semidefinite optimization a specialized cutting and scaling model allows to move from first order towards second order behavior. In Lagrangian relaxation the generation of approximate primal solutions admits primal cutting plane approaches. Based on examples from scheduling, train timetabling and graph partitioning we illustrate a selection of these aspects, highlight some of the theory involved and discuss a few implementational issues arising in the callable library ConicBundle.
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ARRIVAL: A zero-player graph game in NP coNP



Prof. Dr. Bernd Gärtner
ETH Zürich, Schweiz

Time & Place
The presentation on May 30, 2017 will be given in the Lukasklause (Schleinufer 1, 39104 Magdeburg) and starts at 5.00 p.m. (Historischer Raum).

 

Abstract
Suppose that a train is running along a railway network, starting from a designated origin, with the goal of reaching a designated destination. The network, however, is of a special nature: every time the train traverses a switch, the switch will change its position immediately afterwards. Hence, the next time the train traverses the same switch, the other direction will be taken, so that directions alternate with each traversal of the switch.

Given a network with origin and destination, what is the complexity of deciding whether the train, starting at the origin, will eventually reach the destination?

It is easy to see that this problem can be solved in exponential time, but we are not aware of any polynomial-time method. In this talk, I explain where the problem comes from and prove that is is in NP ∩ coNP; actually in UP ∩ coUP (problems with unique NP/coNP certificates).

This raises the question whether people have so far just failed to find a (simple) polynomial-time solution, or whether the complexity status is more subtle, as for some other well-known (two-player) graph games.

Joint work with Jérôme Dohrau, Hagar Mosaad, Manuel Kohler, Jiří Matoušek, Emo Welzl

 Uncertainty quantification in inverse problems

 

Prof. Dr. Claudia Schillings
Universität Mannheim

Time & Place
The presentation on June 27, 2017 will be given in the Lukasklause (Schleinufer 1, 39104 Magdeburg) and starts at 5.00 p.m. (Großer Saal).

 

Abstract
Uncertainty quantification is an interesting, fast growing research area aiming at developing methods to address, characterize and minimize the impact of parameter, data and model uncertainty in complex systems. Applications of uncertainty quantification include all areas of engineering, environmental, physical and biological systems, e.g., groundwater flow problems, shape uncertainties in aerodynamic applications or nano-optics, biochemical networks and finance. The efficient treatment of uncertainties in mathematical models requires ideas and tools from various disciplines including numerical analysis, statistics, probability and computational science. In this talk, we will focus on the identification of parameters through observations of the response of the system - the inverse problem. The uncertainty in the solution of the inverse problem will be described via the Bayesian approach. We will discuss efficient methods to approximate the solution of the resulting high/ infinite dimensional systems.

 

 

Mirjam Dür

Copositive programming: a framework for quadratic and combinatorial optimization

Prof. Dr. Mirjam Dür
Department of Mathematics
University of Trier

Time & Place
The presentation on December 13, 2016 will be given in the Carnot-Gebäude G25 in Room 201 and starts at 5.00 p.m..

Abstract
A copositive optimization problem is a problem in matrix variables with a constraint which requires that the matrix be in the copositive cone. This means that its quadratic form takes nonnegative values over the nonnegative orthant. Many combinatorial problems like for example the maximum clique problem can be equivalently formulated as a copositive problem. Burer (2009) showed that also any nonconvex quadratic problem with linear constraints and binary variables can be reformulated as such a copositive problem. This is remarkable, since by this approach, a nonconvex problem is reformulated equivalently as a convex problem. The complexity of the original problem is entirely shifted into the cone constraint. We review recent progress in this area, concerning both theoretical results and numerical issues. In particular, we show how this approach can be used to deal with the stable set problem for infinite graphs, an application of which is the famous kissing number problem.

 The lecture is part of the 10th CDS anniversary.

 

Manfred Morari

Computation and uncertainty — The past, present and future of control

Prof. Dr. Manfred Morari
Distinguished Faculty Fellow
University of Pennsylvania

Time & Place
The presentation on October 18, 2016 will be given in the Lukasklause (Schleinufer 1, 39104 Magdeburg) and starts at 5.00 p.m. (Historischer Raum).

 

Abstract
Reflecting on our work over the last 40 years I found that it was dominated by two themes: computation and uncertainty. I will describe how the rapidly increasing computational resources have affected our approaches to deal with uncertainty in feedback control. The talk will be illustrated by examples from process control and other application areas like automotive and power systems.

 

Bio
Manfred Morari was head of the Department of Information Technology and Electrical Engineering at ETH Zurich from 2009 to 2012 and head of the Automatic Control Laboratory from 1994 to 2008. Before that he was the McCollum-Corcoran Professor of Chemical Engineering and Executive Officer for Control and Dynamical Systems at the California Institute of  Technology. From 1977 to 1983 he was on the faculty of the University of Wisconsin. He obtained the diploma from ETH Zurich and the Ph.D. from the University of Minnesota, both in chemical engineering.  His interests are in constrained and robust control. Morari’s research is internationally recognized. The analysis techniques and software developed in his group are used in universities and industry throughout the world. He has received numerous awards, including the Eckman Award, Ragazzini Award and Bellman Control Heritage Award from the American Automatic Control Council; the Colburn Award, Professional Progress Award and CAST Division Award from the American Institute of Chemical Engineers; the Control Systems Technical Field Award and the Bode Lecture Prize from IEEE. He is a Fellow of IEEE, AIChE and IFAC. In 1993 he was elected to the U.S. National Academy of Engineering and to the UK Royal Academy of Engineering in 2015. He served on the technical advisory boards of several major corporations.

 

Britta Peis

Matchings and Matroids in Algorithmic Game Theory

Prof. Dr. Britta Peis
RWTH Aachen University
Chair of Management Science

Time & Place
The presentation on October 25, 2016 will be given in the Lukasklause (Schleinufer 1, 39104 Magdeburg) and starts at 5.00 p.m. (Historischer Raum).

 

Abstract
Throughout the talk we will see that the theory of combinatorial optimization turns out to be extremely helpful when it comes to analyzing  game-theoretic models. We focus on the important role of structures and algorithms known from matching-and matroid theory for network bargaining games and congestion games. For example, we will see that congestion games are immune to Braess' paradox if (and only if) each player's strategy space forms the base set of a matroid.

 

Zlatko Drmac

Magdeburg Lectures on Optimization and Control

Prof. Dr. Zlatko Drmac
University of Zagreb

Accurate linear algebra in computational
methods for system and control theory

Tuesday, 05.07.2016 17:00
Lukasklause, Schleinufer 1


Jointly organized by: Faculty of Electrical Engineering and Information Technology, Faculty of Mathematics, Max Planck Institute Magdeburg Center for Dynamic Systems: Biosystems Engineering



Alexander Mitsos

Global Optimization of Unconventional Formulations for Sustainable Energy Systems 

 
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Anton Schiela

Concepts of Function Space Oriented Optimization

Prof. Dr. Anton Schiela
University of Bayreuth


Time & Place
The presentation on January 12, 2016 will be given at the Lukasklause, Schleinufer 1, Magdeburg and starts at 5.00 p.m.
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Samuel Fiorini

No Small Linear Program Approximates Vertex Cover within a Factor 2-ε

Prof. Dr. Samuel Fiorini
Université Libre de Bruxelles


Time & Place
The presentation on December 8, 2015 will be given at the Lukasklause, Schleinufer 1, Magdeburg and starts at 5.00 p.m.
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Greg Blekherman

Nonnegative Polynomials and Sum of Squares

Prof. Greg Blekherman, Ph.D.
Georgia Tech


Time & Place
The presentation on June 30, 2015 will be given in the Otto-von-Guericke-University Magdeburg G03-214 and starts at 5.00 p.m.
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Matthias Heinkenschloss

Trust-region adaptive stochastic collocation for PDE optimization under uncertainty

Matthias Heinkenschloss
Department of Computational and Applied Mathematics
Rice University


Time & Place
The presentation on June 29, 2015 will be given in the Lukas Klause (Schleinufer 1, 39104 Magdeburg) and starts at 5.00 p.m. 
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Benoit Chachuat

Trust-region adaptive stochastic collocation for PDE optimization under uncertainty

Dr. Benoit Chachuat
Faculty of Engineering
Centre for Process Systems Engineering
Department of Chemical Engineering
Imperial College London
UK

Time & Place
The presentation on June 29, 2015 will be given in the Senatssaal (building 05, room 205) and starts at 5.00 p.m.
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Harry L. Trentelman

Model Reduction of Multi-Agent Systems Using Graph Partitions

Prof. Dr. Harry L. Trentelman
Johann Bernoulli Institute for Mathematics and Computer Science
University of Groningen
The Netherlands

Time & Place
The presentation on April 16, 2015 will be given in the Lukasklause (Schleinufer 1, 39104 Magdeburg) and starts at 5.00 p.m.

Abstract
This talk deals with the problem of model reduction of multi-agent systems defined on a graph. Reduced order models are obtained by clustering the vertices (agents) of the underlying communication graph by means of suitable graph partitions. In the reduction process the spatial structure of the network is preserved and the reduced order models can again be realized as multi-agent systems defined on a graph. The agents are assumed to have single-integrator dynamics and the communication graph of the original system is weighted and undirected. The proposed model reduction technique reduces the number of vertices of the graph (which is equal to the dynamic order of the original multi-agent system) and yields a reduced order multi-agent system defined on a new graph with a reduced number of vertices. This new graph is a weighted symmetric directed graph. It is shown that if the original multi-agent system reaches consensus, then so does the reduced order model. For the special case that the clusters are chosen using an almost equitable partition of the graph, we obtain an explicit formula for the H-2 norm of the error system obtained by comparing the input-output behaviors of the original model and the reduced order model. We also prove that the error obtained by taking an arbitrary partition of the graph is bounded from below by the error obtained by using the largest almost equitable partition finer than the given partition. Finally, we extend our results on single integrator dynamics to the case that the agent dynamics is an arbitrary linear input-output system.
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Jan Maciejowski

Fault-tolerant control using Gaussian processes and model predictive control

Prof. Jan Maciejowski
Dep. of Engineering
University of Cambridge
UK

Time & Place
The presentation on January 27, 2015 will be given in the Senatssaal (building 05, room 205) and starts at 5.00 p.m.


Abstract
Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control. Some remarks will be made about the use of a Bayesian framework for studying fault-tolerant control.
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Paul Goulart

Generalized Gauss Inequalities via Semidefinite Programming

Time & Place
The presentation on December 9, 2014 will be given in the Lukasklause (Schleinufer 1, 39104 Magdeburg) and starts at 5.00 p.m.   
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Michael Overton

Fast Approximation of the H∞ Norm via Optimization over Spectral Value Sets

Time & Place
The presentation on November 4, 2014 will be given in the Lukas Klause (Schleinufer 1, 39104 Magdeburg) and starts at 5.00 p.m.
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Prof. Carsten W. Scherer

From Gain-Scheduling to Distributed Control

Time & Place
The presentation on June 16, 2014 will be given at the Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, building 03 - room 106 and starts at 4.45 p.m.
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Vortrag: Prof. Jon Lee, Ph.D.

 Mixed-Integer Quadratic Optimization: Complexity, Algorithms and Computing

Time & Place
The presentation on June 26, 2014 will be given at the Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, building 2 - room 210 and starts at 4 p.m.
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Vortrag: Prof. Dr. Alexander Martin

 
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Vortrag Prof. Bernd Sturmfels

December 4, 2013, 5.30 p.m. at Lukasklause, Schleinufer 1
The Euclidean Distance Degree

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Vortrag Prof. Armin Fügenschuh

November 14, 2013, 5.30 p.m. at Lukasklause, Schleinufer 1

Optimizing Discrete and Continuous Systems over Time

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Sven Leyffer

Recent Advances in Mixed-Integer Nonlinear Optimization
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Friedrich Eisenbrand

Diameter of polyhedra: Abstractions, upper bounds and open problems
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Moritz Diehl

Autogeneration of Nonlinear Optimal Control Algorithms for Embedded Hardware and Application to Tethered Airplane Control
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Markus Schweighofer

Polynomial Optimization via Semidefinite Programming

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Volker Mehrmann

Modelling, Simulation and Optimal Control of Descriptor Systems

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Didier Henrion

Convex Computation of the Region of Attraction of Polynomial Control Systems

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Paul I. Barton

Global Optimization with Differential Equations Embedded

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