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.

Learning Outcomes

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)

Irregular

Pre-requirements

At least 120 ECTS in computer science, preferably including some mathematics

Recommended previous knowledge

INF 270, MAT 112 (Calculus II)

Subject Overlap

I 274: 10 ECTS

Assessment methods

Oral exam.

No aids allowed.

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.