Introduction to Scientific Computing
Spring/Summer 2019, Otto-von-Guericke Universität Magdeburg
Lecturer: Christian Lessig
Lectures: Tuesdays, 13:00-15:00, G29-335
Tutorials: Thursdays, 11:00-13:00, G29-426
The course provides an introduction into important concepts in scientific computing, including the solution of linear systems, least squares problems, matrix decomposition, and (fast) Fourier representations. Each subject will be exemplified by a practical application which is developed in detail and implemented by the students.
- The second assignment has been extended until 6. June, 11:00.
- In the week of 1. June tutorial and lecture are swapped, i.e. the tutorial will take place on Tuesday and the lecture on Thursday.
- Some students had trouble with lib.py with numpy 1.13. Here is a patched version.
- The first assignment is online. Due 10/05/2018.
- There will be no lecture or tutorial in the week of 8. April.
- See the Vorlesungsverzeichnis for details.
Linear algebra recap
Floating point numbers
Least squares, Cholesky, and LU decomposition
Eigen decomposition and diagonalization of matrices
Principal component analysis and singular value decomposition
Week 9 and 10:
Discrete Fourier transform
Introduction to python and Numpy
We will be working with python and the Numpy library
in this course. On Linux you can install it using your package manager. On other operating systems it is convenient to use the Anaconda distribution
which contains all necessary packages.
There are plenty of books on the material covered in the course. Some that I found useful over the years are:
- G. Strang, Lineare Algebra. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.
- G. Strang, Wissenschaftliches Rechnen. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.
- G. H. Golub and C. F. Van Loan, Matrix Computations. The Johns Hopkins University Press, 1996.
- W. Dahmen and A. Reusken, Numerik für Ingenieure und Naturwissenschaftler, second ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008.
- T. Huckle and S. Schneider, Numerik für Informatiker. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002.
Please find the text that best matches your intuition and thinking.