Ye-Chao
Liu
ZIB
Entanglement detection via Frank-Wolfe algorithms
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19. March
Today
Pascal
Weinhart
FU Berlin
Embedding Spanning Trees in (n,d,λ)-graphs via Sorting Networks
Abstract.
My bachelor thesis analyses the paper by Joseph Hyde, Natasha Morrison, Alp Müyesser and MatÃas Pavez-Signé (arXiv:2311.03185v1). The aim is to find arbitrary spanning trees in pseudorandom graphs. This is done by first identifying a large number of long paths within the tree, then embedding the remaining vertices of the tree, then using perfect matchings to embed large parts of the paths and finally using a sorting network to connect the correct ends of the paths. The focus is on the extendability technique, construction of a sorting network and embedding trees.
21. March
Fri W11
Melanie
Reihl
Simultaneous Contact Representations of Planar Graphs
26. March
Wed W12
Jan
Pauls
Universität Münster
Advancing Climate Strategies - High-Resolution Canopy Height Estimation from Space
Abstract.
Reliable and detailed information on forest canopy height is essential for understanding the health and carbon dynamics of forests, which play a pivotal role in climate adaptation and mitigation strategies. Traditional methods of forest monitoring, while foundational, lack the global coverage and are often costly, hindering effective policymaking. Jan Pauls and colleagues have developed a novel framework using satellite data to estimate canopy height on a global scale. The approach combines cutting-edge data preprocessing techniques, a unique loss function to mitigate geolocation inaccuracies, and data filtering from the Shuttle Radar Topography Mission to enhance prediction reliability in mountainous areas. The framework significantly improves upon existing global-scale canopy height maps. By offering a high-resolution (10 m) global canopy height map, the produced map provides critical insights into forest dynamics, aiding in more effective forest management and climate change mitigation efforts. This talk will explore the methods and implications of this work, demonstrating how advancements in Earth observation and machine learning can revolutionize global forest assessments and ecological studies.
02. April
Wed W13
Lukas
Kühne
Bielefeld University
When alcoved polytopes add
Abstract.
Alcoved polytopes are characterized by the property that all facet normal directions are parallel to the roots e_i - e_j. This fundamental class of polytopes appears in several applications such as optimization, tropical geometry or physics.<br>This talk focuses on the type fan of alcoved polytopes which is the subdivision of the metric cone by combinatorial types of alcoved polytopes. The type fan governs when the Minkowski sum of alcoved polytopes is again alcoved. We prove that the structure of the type fan is governed by its two-dimensional faces and give criteria to study the rays of alcoved simplices.<br>This talk is based on joint work with Nick Early and Leonid Monin.
09. April
Wed W14
Sebastian
Knebel
ZIB
Koopman von Neumann mechanics
16. April
Wed W15
Seta
Rakotoamandimby
enpc
Minimization on the sphere and cut selection for the Capra-cutting plane method
Michael
Tsopanopoulos
WIAS
Manuel
Kauers
JKU Linz
D-Finite Functions
Abstract.
A function is called D-finite if it satisfies a linear differential equations with polynomial coefficients. Such functions play a role in many different areas, including combinatorics, number theory, and mathematical physics. Computer algebra provides many algorithms for dealing with D-finite functions. Of particular importance are operations that preserve D-finiteness. In the talk, we will give an overview over some of these techniques.
22. April
Tue W16
Lina
Zhao
City University of Hong Kong
23. April
Wed W16
Yusuke
Kimura
FUJITSU
Multi-node quantum circuit simulation with decision diagram in HPC
Danai
Deligeorgaki
KTH
Colored multiset Eulerian polynomials
Abstract.
The central objects in this talk are the descent polynomials of colored permutations on multisets, referred to as colored multiset Eulerian polynomials. These polynomials generalize the colored Eulerian polynomials that appear frequently in algebraic combinatorics and are known to admit desirable distributional properties, including real-rootedness, log-concavity, unimodality and the alternatingly increasing property. In joint work with Bin Han and Liam Solus, symmetric colored multiset Eulerian polynomials are identified and used to prove sufficient conditions for a colored multiset Eulerian polynomial to satisfy the self-interlacing property. This property implies that the polynomial obtains all of the aforementioned distributional properties as well as others, including bi-gamma-positivity. To derive these results, multivariate generalizations of a generating function identity due to MacMahon are deduced. The results are applied to a pair of questions, both previously studied in several special cases, that are seen to admit more general answers when framed in the context of colored multiset Eulerian polynomials. The first question pertains to s-Eulerian polynomials, and the second to interpretations of gamma-coefficients. We will see some of these results in detail, depending on the pace of the talk. We may also discuss some connections between multiset permutations and polytopes from algebraic statistics.
29. April
Tue W17
Thomas
Creutzig
Erlangen Universität
30. April
Wed W17
Rossella
Giorgio
TU Wien
Anastasia
Giorgi
University of Calgary
Kevin
Kühn
Goethe University Frankfurt
07. May
Wed W18
Rossella
Giorgio
TU Wien
13. May
Tue W19
Leonid
Chekhov
Michigan State University
14. May
Wed W19
Pierluigi
Colli
Università Pavia, Italy
Andrea
Signori
Politecnico di Milano, Italy
21. May
Wed W20
Franziska
Eberle
MATH+ Junior Research Group Leader
Demand Strip Packing
04. June
Wed W22
11. June
Wed W23
Martin
Brokate
WIAS and TU München
18. June
Wed W24
Christophe
Roux
ZIB
Bounding geometric penalties in Riemannian optimization
Abstract.
Riemannian optimization refers to the optimization of functions defined over Riemannian manifolds. Such problems arise when the constraints of Euclidean optimization problems can be viewed as Riemannian manifolds, such as the symmetric positive-definite cone, the sphere, or the set of orthogonal linear layers for a neural network. This Riemannian formulation enables us to leverage the geometric structure of such problems by viewing them as unconstrained problems on a manifold. The convergence rates of Riemannian optimization algorithms often rely on geometric quantities depending on the sectional curvature and the distance between iterates and an optimizer. Numerous previous works bound the latter only by assumption, resulting in incomplete analysis and unquantified rates. In this talk, I will discuss how to remove this limitation for multiple algorithms and as a result quantify their rates of convergence.
02. July
Wed W26
Benjamin
Gess
TU Berlin and MPI Leipzig
04. July
Fri W26
Silvia
De Toffoli
IUSS Pavia
Kovalevskaya Lecture
06. July
Sun W26
09. July
Wed W27
Amru
Hussein
Universität Kassel
16. July
Wed W28
Alice
Marveggio
Hausdorff Center for Mathematics Universität Bonn