Algorithms of Scientific Computing - Summer 12

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Term
Summer 12
Lecturer
Michael Bader, Dirk Pflüger
Time and Place
Mondays 10:15-11:45 and Thursdays 8:45-10:15, room MI 02.07.023, starting 16/04/2012
Tutorial: Wednesdays 10:15-11:45, room MI 02.07.023 (on Wed 18/04/2012 lecture instead of tutorial)
Audience
Modul IN2001
Informatik Diplom: Wahlpflichtfach im Bereich theoretische Informatik
Informatik Master: Wahlfach im Fachgebiet "Algorithmen und Wissenschaftliches Rechnen"
Informatik Master: Elective topic, subject area "Algorithms and Scientific Computing"
Informatik/Wirtschaftsinformatik Bachelor: Wahlfach
Studierende der Mathematik/Technomathematik, Natur- und Ingenieurwissenschaften
Students of CSE (Application Catalogue E1)
Tutorials
Gerrit Buse, Kaveh Rahnema
Exam
written exam at the end of the semester (details will follow)
Semesterwochenstunden / ECTS Credits
6 SWS (4V + 2Ü) / 8 Credits
TUMonline
Algorithms of Scientific Computing



What's ASC about?

Many applications in computer science require methods of (prevalently numerical) mathematics - especially in science and engineering, of course, but as well in surprisingly many areas that one might suspect to be directly at the heart of computer science:

Consider, for example, Fourier and wavelet transformations, which are indispensable in image processing and image compression. Space filling curves (which have been considered to be "topological monsters" and a useless theoretical bauble at the end of the 19th century) have become important methods used for parallelization and the implementation of data bases. Numerical methods for minimization and zero-setting are an essential foundation of Neural Networks in machine learning.

Essentially, these methods come down to the question of how to represent and process information or data as (multi-dimensional) continuous functions. Algorithms of Scientific Computing (former Algorithmen des Wissenschaftlichen Rechnens) provides a generally understandable and algorithmically oriented introduction into the foundations of such mathematical methods. Topics are:

  • The fast Fourier transformation (FFT) and some of its variants:
    • FCT (Fast Cosine Transform), real FFT, Application for compression of video and audio data
  • Space filling curves (SFCs):
    • Construction and properies of SFCs
    • Application for parallelization and to linearize multidimensional data spaces in data bases
  • Hierarchical and recursive methods in scientific computing
    • From Archimede's quadrature to the hierarchical basis
    • Cost vs. accuracy
    • Sparse grids, wavelets, multi-grid methods

News

Material

Lecture slides and worksheets will be published here as soon as they become available.

Fast Fourier Transform

Worksheets and Solutions

Number Topic Worksheet Date Maple/Python scripts Solution
1 Discrete Fourier Transform Worksheet 1 25.4.2012 --- ---

Literature

Fast Fourier Transform:

The lecture is oriented on:

  • W.L. Briggs, Van Emden Henson: The DFT - An Owner's Manual for the Discrete Fourier Transform, SIAM, 1995
  • Thomas Huckle, Stefan Schneider: Numerische Methoden - Eine Einführung für Informatiker, Naturwissenschaftler, Ingenieure und Mathematiker, Springer-Verlag, Berlin-Heidelberg, 2.Auflage 2006 (German only)
  • Charles van Loan: Computational Frameworks for the Fast Fourier Transform, SIAM, 1992

Hierarchical Methods and Sparse Grids

Space-filling Curves:

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