Algorithms for Scientific Computing

What it is about

You will learn efficient, hierarchical algorithms and data structures for scientific computing and how to implement them. The focus is on fast discrete Fourier and related transforms (FFT, DCT/DST), space-filling curves (Peano, Hilbert) for organizing multi-dimensional data, as well as hierarchical methods such as Sparse Grids and adaptive representations. In the end you will be able to explain such procedures, analyze them, and implement them if needed.

What you will be able to do

• Knowledge and implementation capability of selected hierarchical methods
• Analysis of runtime costs and—where applicable—accuracy
• Comparison of these methods with alternative procedures
• Transfer of the methodology to related questions

What the module consists of

• LectureConveying the content through lectures and presentations
• Exercise/TutorialSolving concrete problems, partly in teams, deepening selected examples

Teaching method

• Lectures/PresentationsExplanation of theory and algorithms
• Self-study of literatureDeepening of topics and independent engagement
• Tutorials with assignments and teamworkApplication of methods to concrete problems