Course Syllabus for

Numerical Linear Algebra
Numerisk linjär algebra

FMNN01F, 7.5 credits

Valid from: Spring 2024
Decided by: Maria Sandsten
Date of establishment: 2023-10-26

General Information

Division: Numerical Analysis
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: FMNN01
Teaching language: English


The aim of the course is to make the postgraduate student familiar with concepts and methods from numerical linear algebra. In general there are ready-made program libraries available but it is important to be able to recognize types of input which may cause problems for the most common methods.


Knowledge and Understanding

For a passing grade the doctoral student must have demonstrated substantially better and more useful knowledge of numerical linear algebra than students who only have completed a regular basic course in scientific computing or linear algebra.

Competences and Skills

For a passing grade the doctoral student must be able to implement algorithms for numerical linear algebra algorithms as computer code and to use them to solve applied problems.

Judgement and Approach

For a passing grade the doctoral student must write logically well-structured reports, in adequate terminology, on weekly homework dealing with the construction and application of advanced algorithms in linear algebra.

Course Contents

Norms. Singular value decomposition and numerical rank. QR factorization, the Gram-Schmidt process and Householder matrices. Least squares problems and pseudoinverses. Linear systems of equations and condition numbers. Positive definite matrices and Cholesky factorization. Numeric determination of eigenvalues.

Course Literature

Trefethen, Lloyd N. & David Bau, I.: Numerical Linear Algebra. SIAM, 1997. ISBN 9780898713619.

Instruction Details

Type of instruction: Lectures. Voluntary assignments are given during the course. Feedback is given to those who hand in solutions.

Examination Details

Examination format: Oral exam
Grading scale: Failed, pass

Admission Details

Assumed prior knowledge: Calculus in several variables. Linear algebra including eigenvalues/vectors. Programming in Matlab or Python.

Course Occasion Information

Contact and Other Information

Course coordinators:
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