Forschungsseminar Algorithmische Optimierung (AGs Hante/Walther)
Ort: Rudower Chaussee 25, Raum 2.417
Zeit: Donnerstag, 15:15 Uhr
Studierende und Gäste sind herzlich willkommen.
Vorträge im Wintersemester 2023/2024
27.11.2023 |
Verteidigung Masterarbeit Gloria Xiao Beginn 12:00 Achtung: Geänderter Tag und Uhrzeit! |
28.11.2023 | Verteidigung Masterarbeit Arsen Hnatiuk |
Stochastic Aspects of Dynamical Low-Rank Approximation in the Context of Machine Learning | |
Beginn: 13:15 | |
Achtung: Geänderter Tag und Uhrzeit! | |
30.11.2023 | Sonja Steffensen, Institut für Geometrie und Praktische Mathematik, RWTH Aachen |
On Multilevel Game Theory and its Applications 🖉
Hierarchical Nash game models are an important modelling tool in various applications to study a strategic non-cooperative decision process of individuals, where the individuals can be split into a hierarchy of at least two different groups. Such models are in general mathematically described by multilevel games. |
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14.12.2023 | Gabriele Eichfelder, Fachgebiet Mathematische Methoden des Operations Research, TU Ilmenau |
Multiobjective Mixed Integer Programming 🖉
Multiobjective mixed integer nonlinear optimization refers to mathematical programming problems where more than one nonlinear objective function needs to be optimized simultaneously and some of the variables are constrained to take integer values. In this talk, we give a short introduction to the basic concepts of multiobjective optimization. We give insights why the famous approach of scalarization might not be an appropriate method to solve these problems. Instead, we present two procedures to solve the problems directly. The first is a branch-and-bound method based on the use of properly defined lower bounds. We do not simply rely on convex relaxations, but we built linear outer approximations of the image set in an adaptive way. The second method is tailored for convex objective functions and is purely based on the criterion space. It uses ingredients from the well-known outer approximation algorithm from single-objective mixed-integer optimization and combines them with strategies to generate enclosures of nondominated sets by iteratively improving approximations. For both algorithms, we are able to guarantee correctness in terms of detecting the nondominated set of multiobjective mixed integer problems according to a prescribed precision. |
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18.01.2024 | Bennet Gebken, Universität Paderborn |
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First- and second-order descent methods for nonsmooth optimization based on deterministic gradient sampling 🖉
In nonsmooth optimization, it is well known that the classical gradient is not a suitable object for describing the local behavior of a function. Instead, generalized derivatives from nonsmooth analysis, like the Clarke subdifferential, have to be employed. While in theory, the Clarke subdifferential inherits many useful properties from the classical gradient, there is a large discrepancy in practice: It is unstable, and for a general locally Lipschitz continuous function, it is impossible to compute. Thus, in practice, the Clarke subdifferential has to be approximated. A simple strategy to achieve this, known as gradient sampling, is based on approximating it by taking the convex hull of classical gradients evaluated at smooth points from a small neighborhood of a given point. |
01.02.2024 | Moritz Link, Universität Konstanz |
Multiobjective mixed-integer nonlinear
optimization with application to energy supply networks 🖉 In light of the ongoing developments in the climate crisis, it is necessary to consider factors beyond the sole economic perspective in energy supply network planning. This gives rise to a classical multiobjective optimization problem in- |
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08.02.2024 | Vladimir Shikhman, TU Chemnitz |
TBA | |
22.02.2024 |
Verteidigung Masterarbeit Enrico Bergmann Beginn 8:30 Achtung: Geänderter Tag und Uhrzeit! |
22.02.2024 |
Verteidigung Masterarbeit Elisa Giesecke Beginn 13:00 Achtung: Geänderter Tag und Uhrzeit! |
weitere Termine folgen | |