Linear systems of equations: direct algorithms, perturbation theory and condition, rounding errors. Sparse matrices.
Iterative methods: stationary iterations, Krylov space methods and preconditioning.
Eigenvalue problems: theory, transformation methods and iterative methods.
Singular value decomposition and its applications in data analysis and information retrieval.
Model reduction for linear and nonlinear dynamical systems.
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.
After having completed the course the student should realize how linear algebra is depending on computer resources and accuracy when performing a scientific computation. The student should also be able to utilize modern computing routines from linear algebra in a practical problem.
After the course the student should be able to
- identify linear algebra computations in a practical problem
- perform such a computation, estimate computer resources and judge the quality of the results
- implement special algorithms adapted to the properties of the problem
- design the algorithm so that that the machine architecture of the computers can be utilized.