Recommended prerequisite for participation in
the module
The module builds on knowledge obtained by the modules Linear
Algebra with Applications, Analysis 1, and Probability Theory from
the BSc in Mathematics or similar.
Content, progress and pedagogy of the
module
Learning objectives
Knowledge
- have knowledge of floating point arithmetic, including the
international standards for floating point arithmetic
- have knowledge of error analysis and stability of numerical
algorithms
- have knowledge of polynomial interpolation and its application
to the derivation of numerical algorithms
- have knowledge of basic results in approximation theory
- have knowledge of methods for finding zeroes of functions
- have knowledge of numerical linear algebra, in particular
algorithms adapted to large sparse systems of linear equations
- have knowledge of methods for numerical differentiation,
including spectral methods
- have knowledge of methods for numerical integration, including
Gaussian quadrature
- have knowledge of numerical solution methods for ordinary
differential equations, including spectral methods
- have knowledge of some probabilistic methods in numerical
analysis, including Monte-Carlo methods
Skills
- can implement basic numerical algorithms in different computer
architectures
- can choose appropriate numerical methods to solve a given class
of problems
Competences
- can evaluate the appropriateness of a given numerical method
for solving a class of problems
- are aware of the limitations of numerical methods to solve a
class of problems
Extent and expected workload
This is a 5 ECTS project module and the work load is
expected to be 150 hours for the student.
Exam
Exams
Name of exam | Numerical Analysis |
Type of exam | Oral exam |
ECTS | 5 |
Assessment | Passed/Not Passed |
Type of grading | Internal examination |
Criteria of assessment | The criteria of assessment are stated in the Examination
Policies and Procedures |