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Inhalt des Dokuments

Absolventen-Seminar • Numerische Mathematik

Absolventen-Seminar
Verantwortliche Dozenten:
Prof. Dr. Christian Mehl [1], Prof. Dr. Volker Mehrmann [2]
Koordination:
Benjamin Unger [3], Dr. Matthias Voigt [4]
Termine:
Do 10:00-12:00 in MA 376
Inhalt:
Vorträge von Diplomanden, Doktoranden, Postdocs und manchmal auch Gästen zu aktuellen Forschungsthemen
Wintersemester 2017/2018 Vorläufige Terminplanung
Datum
Zeit
Raum
Vortragende(r)
Titel
Do 19.10.
10:15
Uhr
MA 376
Vorbesprechung
Do 26.10.
10:15
Uhr
MA 376
Ines Ahrens
A generalization of the Pantelides algorithm for DAEs with delay [abstract]
Murat Manguoglu [5]
Parallel Solution of Sparse Underdetermined Linear Least Squares Problems [abstract]
Do 02.11.
10:15 Uhr
MA 376
no seminar
Do 09.11.
10:15
Uhr
MA 376
Benjamin Unger [6]
Do 16.11.
10:15
Uhr
MA 376
Riccardo Morandin [7]
Felix Black
Do
23.11.
10:15
Uhr
MA 376
Do
30.11.
10:15
Uhr
MA 376
Do 07.12.
10:15
Uhr
MA 376
Murat Manguoglu [8]
Benjamin Unger [9]
Do 14.12.
10:15
Uhr
MA 376
Matthew Salewski [10]
Christian Mehl [11]
Do 21.12.
10:15
Uhr
MA 376
Carlo Cassina
Jeroen Stolwijk [12]
Do 11.01.
10:15
Uhr
MA 376
Philipp Schulze [13]
Marine Froidevaux [14]
Do 18.01.
10:15 Uhr
MA 376
Arbi Moses Badlyan [15]
David Noben
Do 25.01.
10:15
Uhr
MA 376
David Kohn
Christoph Zimmer [16]
Do 01.02.
10:15
Uhr
MA 376
Daniel Bankmann [17]
Sophia Bikopoulou [18]
Do 08.02.
10:15 Uhr
MA 376
Sarah Hauschild
Andres Gonzales Zumba [19]
Di 15.02.
10:15 Uhr
MA 376
Hannes Gernandt [20]
Volker Mehrmann [21]

Rückblick

  • Absolventen Seminar SS 17 [22]
  • Absolventen Seminar WS 16/17 [23]
  • Absolventen Seminar SS 16 [24]
  • Absolventen Seminar WS 15/16 [25]
  • Absolventen Seminar SS 15 [26]
  • Absolventen Seminar WS 14/15 [27]
  • Absolventen Seminar SS 14 [28]
  • Absolventen Seminar WS 13/14 [29]
  • Absolventen Seminar SS 13 [30]
  • Absolventen Seminar WS 12/13 [31]
  • Absolventen Seminar SS 12 [32]
  • Absolventen Seminar WS 11/12 [33]

Abstracts zu den Vorträgen:

Ines Ahrens (TU Berlin)

Donnerstag, 26. Oktober 2017

A generalization of the Pantelides algorithm for DAEs with delay

The strangeness index for DAEs is based on the derivative array, which consists of the system itself plus its time derivatives. Index reduction is performed by selecting certain important equations from the derivative array. In a large-scale setting with high index, this might become computationally infeasible. However, if it is known a priori which equations of the original systems need to be differentiated, then the computational cost can be reduced. One way to determine these equations is by means of the Pantelides algorithm.

If the DAE features in addition a delay term then taking derivatives might not be sufficient and instead, the derivative array must additionally be shifted in time thus increasing the computational complexity even further. In this talk I will explain how one can modify the Pantelides algorithm such that it determines the equations which need to be shifted and/or differentiated. This is joint work with Benjamin Unger.

Murat Manguoglu (Middle East Technical University)

Donnerstag, 26. Oktober 2017

Parallel Solution of Sparse Underdetermined Linear Least Squares Problems

Sparse underdetermined systems of equations in which the minimum norm solution needs to be computed arise in many applications, such as geophysics, signal processing, and computational finance. In this talk, we introduce a parallel algorithm for obtaining the minimum 2-norm solution of sparse underdetermined system of equations. The proposed algorithm assumes a generalized banded form where the coefficient matrix has a column overlapped block structure in which the blocks are sparse. The blocks are handled independently by any existing solver and a smaller reduced system is formed and needs to be solved before obtaining the minimum norm solution of the original system in parallel. We implement the proposed algorithm by using the message passing paradigm. We show the parallel scalability of the proposed algorithm and compare it against an existing state-of-the-art solver on both shared and distributed memory platforms. This is a joint work with F. Sukru Torun (Bilkent University) and Cevdet Aykanat (Bilkent University).

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