- Well-posed and ill-posed problems and conditioning of problems
- Rounding errors and numerical stability
- Linear systems of equations: direct methods, sparse matrices
- Singular value decomposition and its application in data analysis and information retrieval
- Eigenvalue problems: theory, orthogonal transformations, iterative methods
- Iterative methods for large linear systems: stationary iterations, Krylov space methods, preconditioning.
For each algorithm it is studied how it works, how many resources that are used as well as how good accuracy that can be expected in the results.