Graphics & Geometry Group

Scientific Computing

Lecturer: Martin Komaritzan, Jan Philip Göpfert
Lecture: Wed, 10-12, Room D2-136
Exercise: Tue, 8-10, Room V2-222, V2-229
eKVV: 392022
Credits: 5 points
Scientific Computing


Many interesting projects in natural sciences and engineering require the computation of numerical solutions to certain mathematical problems, such as solving systems of equations or minimizing some cost function. This course introduces the most frequently used numerical methods in a compact manner, based on intuitive and interesting examples from computer graphics and physics-based dynamic simulations.

We will not focus on the theoretical derivation of the presented techniques. Instead, our goal is to effciently and robustly solve numerical problems in practical applications, which requires these three steps:

  1. Given an engineering problem, formulate it as a mathematical problem, for instance as a system of equations or an optimization problem.
  2. Given a mathematical problem, analyze its properties to understand which numerical methods can be employed for its solution.
  3. Given a numerical method, know which open-source implementation can be used and/or how to implement it yourself as an efficient and robust algorithm.

The numerical methods to be discussed include solving dense and sparse linear systems, least squares approximations, and partial differential equations. We will also discuss efficient C++ programming and shared memory parallelization.

To facilitate a better understanding we will implement most of the techniques that we discuss in the lecture in the programming assignments. Our exercises therefore consist of several mini-projects, which you can work on alone or in groups. Our tutors have weekly consulting hours, where students can get help if they have trouble with the implementation. At the end of each mini-project, students will present their results in the exercise course.




Week Lecture (Wednesday) Exercise (Tuesday)
16 Introduction
17 Linear Systems, LU Factorization
18 Least Squares, Cholesky Factorization Curve Interpolation, C++ Introduction
19 QR Factorization
20 SVD, Numerical Stability Curve Approximation
21 Heat Equation, Time Integration
22 Laplace Equation, Gradient Descent Diffusion
23 Conjugate Gradients, Sparse Matrices
24 Efficient C++, SIMD,
Intel SIMD Intrinsics, Performance Test Code
Laplace Equation
25 Parallel Computing, OpenMP
26 GPU Computing Parallelization
27 Wave Equation, Band Cholesky
28 Sparse Cholesky Factorization Wave Equation
29 TBA
30 Conclusion Conclusion