Table of contents

The course is connected to the following study programs

Teaching language

Norwegian or English

Recommended previous knowledge

60 sp of Mathematics (including Linear Algebra) and at least one subject at 200 level. Basic knowledge of a programming language (Python, MatLab, R etc.).

Learning outcomes

Upon successful completion of the course, the student will be able to

  • Have knowledge about factorization techniques for matrices
  • Know how to solve linear systems with large quadratic matrices
  • Have knowledge about the least squares method for large rectangular matrices
  • Know how to find eigenvalues and eigenvectors of large matrices
  • Construct algorithms in a programming language
  • Analyse stability and convergence of computer algorithms

Contents

Diagonalization, LU-, QR- and singular value factorization. Matrix norms. Least squares method. Iterative methods and fixed-points for solving linear equations (Jacobi and Gauss-Seidel). Preconditioning. Gradient method and Krylov-spaces. Application in e.g. Fourier-transform, partial differential equations, splines and/or machine learning.

Teaching and learning methods

Lectures, group work, and mandatory hand-ins. Estimated workload of the course is 267 hours.

Examination requirements

Mandatory hand-ins must be approved. See Canvas for details.

Examinations

Individual written exam or individual oral exam, which counts 100 % towards the final grade. Graded assesment.

The form of examination depends on the number of students and will be announced by the lecturer no later than October 1st.

Student evaluation

The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.

Offered as a free-standing course

Yes. Subject to availability or capacity.
Last updated from FS (Felles studentsystem) Oct. 15, 2026 2:31:25 PM