Algorithms for Scientific Computing - Summer 18

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Term
Summer 2017
Lecturer
Univ.-Prof. Dr. Michael Bader
Time and Place
Lecture:tba, MI Hörsaal 2
Tutorial: tba
Audience
see module description (IN2001) in TUMonline
Tutorials
Michael Obersteiner, M.Sc., Jean-Matthieu Gallard, M.Sc.
Exam
tba
Semesterwochenstunden / ECTS Credits
6 SWS (4V + 2Ü) / 8 Credits
TUMonline
https://campus.tum.de/tumonline/wbLv.wbShowLVDetail?pStpSpNr=950290914



News & Announcements

What's ASC about?

Many applications in computer science require methods of (numerical) mathematics - especially in science and engineering, of course, but also 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. Similar, numerical methods for approximation have become essential techniques for high-dimensional classification problems in data science. Essentially, these methods come down to the question of how to represent and process information or data as (multi-dimensional) continuous functions. "Algorithms for Scientific Computing" thus provides an algorithmically oriented introduction to the foundations of such mathematical methods.

Topics include:

  • The fast Fourier transformation (FFT) and some of its variants:
    • FCT (Fast Cosine Transform), real FFT, Application for compression of video and audio data
  • Hierarchical and recursive methods in scientific computing
    • From Archimedes' quadrature to the hierarchical basis
    • Classification problems
    • From the hierarchical basis to wavelets
  • High-demonsional problems
    • Sparse grids and the sparse-grid combination technique
  • Octrees and Space filling curves (SFCs):
    • Tree-structured (hierarchical) adaptivity
    • Construction and properies of SFCs
    • Application for parallelization and to linearize multidimensional data spaces in data bases

Lecture Slides and Supplementary Materials

Lecture slides are published here successively. For future lectures, the respective slides from summer 2017 will be linked.

Fast Fourier Transform

Hierarchical Methods

Sparse Grids

Space-Filling Curves

Worksheets and Solutions

Number Topic Worksheet Tutorial Solution
- Mock Exam Mock exam -

Mock exam solution


Jupyter Notebook

Repeat Exam

Literature and Additional Material

Books that are labeled as "available as e-book" can be accessed as e-book via the TUM library - see the ebooks website of the library for details on how to access the books.

Fast Fourier Transform:

The lecture is oriented on:

Hierarchical Methods and Sparse Grids

Wavelets

Space-filling Curves:

Background Material Concerning Scientific and High Performance Computing