Course content

The course focuses on iterative techniques for solving large sparse linear systems of equations which typically stem from the discretisation of partial differential equations. In addition, computation of eigenvalues, least square problems and error analysis will be discussed.

Learning outcome

The course should prepare the students to master the solution of large linear systems and eigenvalue problems by the most modern computers and software available at any time. They should also possess extensive knowledge of the theory behind the solution technques after taking this course.

Learning methods and activities

Lectures, projects-/semester problem and exercises. The exercises demand the use of a computer. Portfolio assessment is the basis for the grade awarded in the course. This portfolio comprises a written final examination (70%) and exercises (30%). The results for the constituent parts are to be given in %-points, while the grade for the whole portfolio (course grade) is given by the letter grading system. Retake of examination may be given as an oral examination. The lectures will be given in English if they are attended by students from the Master's Programme in Mathematics for International students.

Compulsory assignments

• Exercises

Recommended previous knowledge

The course is based on TMA4145 Linear Methods or equivalent. TMA4215 Numerical Mathematics is recommended, but not mandatory.

Course materials

Will be announced at the start of the course.

Credit reductions