After the  course the student should:

1. Knowledge and understanding

• Be able to explain  how different  sources of errors affect the accuracy in computations.
• Be able to explain  basic techniques, such as linearization and discretization, for numeric computing.

2. Skills and abilities

• Be able to use numerical methods for solving advanced computational problems, such as  e.g. optimization problems and  partial differential equations.
• Be able to implement different methods on a computer and to use existing software, e.g., matlab.

3. Assessment  and attitudes

• Be able to judge different methods regarding strengths, weaknesses and usability.
• Be able to judge the reliability of computed results.
• Be oriented about current research in the areas.

Numerical  methods for  linear and nonlinear optimization  and for  ordinary and partial differential equations.

Lectures  and supervision in connection to assignments.

Most part of the studies are performed, outside the scheduled lectures, by working with different assignments were different algorithms are implemented and analysed.

The students will here be trained in understanding and implementing different algorithms and to judge them. The students are also trained in structuring problems and to communicate  computational  issues and how to solve them.

Assignments  and  an exam  that can be written or oral.