The course is connected to the following study programs

Teaching language

Norwegian or English

Recommended prerequisites

60 sp of Mathematics (including MA-185) and at least one subject at 200 level. Basic knowledge of a programming language (Python, MatLab, R etc.).

Course 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.

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

Examination requirements

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

Teaching methods

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

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.

Admission for external candidates

No

Offered as Single Standing Module

Yes. Subject to availability or capacity.

Assessment methods and criteria

Written, supervised, and graded exam, 5 hours.

Last updated from FS (Common Student System) June 30, 2024 5:42:46 PM