Postgraduate course

Scientific Computing 2

  • ECTS credits10
  • Teaching semesterSpring
  • Course codeMAT260
  • Number of semesters1
  • Language


  • Resources

Semester of Instruction


Objectives and Content

The course gives an introduction to algorithms and theory for numerical solution of systems of ordinary differential equations, iterative solution of systems of non-linear equations and basic methods for calculating eigenvalues. Computation of the best approximation in the least square theory with focus on orthogonal polynomials and trigonometric approximation are also treated. In addition one looks at special problems in numerical integration and Gauss quadrature.The course also deals with differential methods for initial value problems, Runge Kuta and multistep methods for time integration.

Learning Outcomes

After completed course, the students are expected to be able to

  • explain and use the power method to find the smallest and the biggest eigenvalue of a matrix
  • show Schur's and Gershgorin's theorem for matrices
  • explain and use iterative methods for nonlinear systems like fix point method and Newton's method
  • explain the least square method for determining the best approximation
  • explain the Gauss-quadrature for approximating integrals
  • describe and use the Runge-Kutta methods and the multistep methods for solving numerically ordinary differential equations

Required Previous Knowledge


Recommended Previous Knowledge

MAT160 Scientific computing I

Forms of Assessment

Written exam. Exercises might be graded and included in the final grade. It may be oral examination if less than 20 students attend the course.

Grading Scale

The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.


Contact Information


Exam information