Non-Linear Optimization
Course offered :
- Current semester
- Next semester
Course offered by
| Number of credits | 10 |
| Course offered (semester) | Irregular |
| Subject overlap | I 274: 10 ECTS |
| Schedule | Schedule |
| Reading list | Reading list |
Language of Instruction
English
Pre-requirements
At least 120 ECTS in computer science, preferably including some mathematics
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
Language of Instruction
English
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.
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.