SF3584 Preconditioning for Linear Systems 7.5 credits
Förkonditionering för linjära ekvationssystem
Education cycleThird cycle
Main field of study
Information for research students about course offerings
Intended learning outcomes
A students which has passed this course should know
which iterative methods are available for linear systems, and integration of preconditioning.
how to apply and adapt convergence theory for the iterative methods.
apply general preconditioners based on, diagonal, LU-factorization.
apply problem specific preconditioners, such as domain decomposition, Schur-complement and adapted for partial differential equations such as Helmholtz problem.
characterize the quality of a preconditioner experimentally and theoretically.
Course main content
- Iterative methods (Krylov methods, Gauss-Seidel methods)
- Convergence theory (eigenvalues, pseudospectra, right-hand side dependence)
- General preconditioners
- Problem specific preconditioners
Lectures, seminar, problem solving, problem composition.
This course is designed for PhD students in applied and computational mathematics, but it is suitable also for other PhD students with a background in computation with mathematical interests. The students are expected to have taken a basic and a continuation course in numerical analysis or acquired equivalent knowledge in a different way, and preferrably also a course in matrix computations or numerical linear algebra, e.g., SF3580 and/or SF2524.
It will be announced on the course web page 3 weeks before course starts
- INL1 - Assignment, 7.5, grading scale: P, F
Requirements for final grade
Problems solved, posed, seminar presented and homeworks solved.
Elias Jarlebring (email@example.com)
Elias Jarlebring <firstname.lastname@example.org>
Course syllabus valid from: Spring 2019.
Examination information valid from: Autumn 2018.