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Inhalt des Dokuments

Absolventen-Seminar • Numerische Mathematik

Absolventen-Seminar
Verantwortliche Dozenten:
Prof. Dr. Christian MehlProf. Dr. Volker Mehrmann
Koordination:
Benjamin Unger, Dr. Matthias Voigt
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
Murat Manguoglu
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
Do 16.11.
10:15
Uhr
MA 376
Riccardo Morandin
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
Benjamin Unger
Do 14.12.
10:15
Uhr
MA 376
Matthew Salewski
Christian Mehl
Do 21.12.
10:15
Uhr
MA 376
Carlo Cassina
Jeroen Stolwijk
Do 11.01.
10:15
Uhr
MA 376
Philipp Schulze
Marine Froidevaux
Do 18.01.
10:15 Uhr
MA 376
Arbi Moses Badlyan
David Noben
Do 25.01.
10:15
Uhr
MA 376
David Kohn
Christoph Zimmer
Do 01.02.
10:15
Uhr
MA 376
Daniel Bankmann
Sophia Bikopoulou
Do 08.02.
10:15 Uhr
MA 376
Sarah Hauschild
Andres Gonzales Zumba
Di 15.02.
10:15 Uhr
MA 376
Hannes Gernandt
Volker Mehrmann

Abstracts zu den Vorträgen:

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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