Aim and Content
The course includes the basic framework for constructing efficient methods for solving unconstrained optimization problems. Topics include line search and trust regions methods for unconstrained optimization. Topics in constrained optimization include the Karush-Kuhn-Tucker theory and basic solution techniques.
Upon completion of INF272 Non-Linear Optimization, the student is supposed to be able to
- explain how a continuous optimization problem can be solved
- analyze the efficiency of the solution methods
- explain the mathematical theory underlying the algorithms for continuous optimization problems.
Course offered (semester)
At least 120 ECTS in computer science, preferably including some mathematics
Recommended previous knowledge
INF 270, MAT 112 (Calculus II)
I 274: 10 ECTS
No aids allowed.
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.