MM863: Selected topics in numerical anlysis I

Study Board of Science

Teaching language: English
EKA: N310069102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Master

STADS ID (UVA): N310069101
ECTS value: 5

Date of Approval: 11-10-2021

Duration: 1 semester

Version: Approved - active


Co-taught with MM566.

Entry requirements

The course cannot be taken by students who have followed or passed MM566

Academic preconditions

Students taking the course are expected to:

  • have knowledge of elementary mathematical background as provided by the courses Calculus, Linear Algebra, Mathematical and Numerical Analysis and Ordinary Differential Equations

Basic skills in scientific programming may be helpful but are not mandatory

Course introduction

The aim of the course is to enrich the students' skills and range in numerical analysis and applied mathematics and to show prospects for possible thesis topics and to enable the student to:   
  • understand advanced principles of numerical thinking
  • understand and work with numerical analysis in a broad range of applications
  • compare and contrast the methods introduced in the course
  • transfer the learning content to new problems
  • to make use of the techniques in practical applications

In relation to the competence profile of the degree it is the explicit focus of the course to:

  • Provide the competence to analyze and apply mathematical models
  • Provide a thorough understanding on the interplay between theoretical development, feasibility and computational efficiency.

Expected learning outcome

The learning objective of the course is that the student demonstrates the ability to:
  • know the definitions of the quantities and terms that were introduced in the lecture.
  • relate the main purpose and raison d'être of every section of the whole lecture
  • know the key ideas for the derivation of the main theorems and algorithms that are introduced in the lecture
  • demonstrates the ability to cover some selected topics in full detail, including proof techniques
  • adapt and transfer known concepts to new, related application scenarios
  • to formulate the problems (including proofs) in a correct and formal mathematical language
  • to analyze, apply and modify the introduced techniques


The following main topics are contained in the course: Introduction to one or more topics in numerical analysis.
This could, for example, be:

  • Matrix analysis and matrix decompositions
  • Iterative solutions of linear equation systems
  • Iterative solutions of eigenvalue problems
  • Design and analysis of computer experiments
  • Model reduction
  • Matrix manifolds and applications


See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)








Second examiner: Internal


7-point grading scale


Student Identification Card


Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value


Indicative number of lessons

42 hours per semester

Teaching Method

At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase. These teaching activities are reflected in an estimated allocation of the workload of an average student as follows:

  • Intro phase: 28 hours
  • Skills training phase: 14 hours, hereof tutorials: 14 hours

Activities during the study phase:

  • Reading of suggested literature
  • Preparation of exercises in study groups
  • Contributing to online learning activities related to the course

Teaching is centered on interaction and dialogue. In the intro phase, concepts, theories and models are introduced and put into perspective. In the training phase, students train their skills through exercises and dig deeper into the subject matter. In the study phase, students gain academic, personal and social experiences that consolidate and further develop their scientific proficiency. Focus is on immersion, understanding, and development of collaborative skills.

Teacher responsible

Name E-mail Department
Kristian Debrabant Institut for Matematik og Datalogi


Administrative Unit

Institut for Matematik og Datalogi (matematik)

Team at Educational Law & Registration


Offered in


Recommended course of study

Profile Education Semester Offer period

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study. 
If a transitional arrangement has been made for a course, it will be stated in the list. 
See transitional arrangements for all courses at the Faculty of Science.