Student Pages
Postgraduate course

Nonlinear Optimization

  • ECTS credits10
  • Teaching semesterSpring, Autumn
  • Course codeINF272
  • Number of semesters1
  • Language


  • Resources

Main content

Level of Study


Teaching semester

Irregula. Runs springsemester 2023.

Objectives and Content

The course contains the basic framework for constructing efficient methods for solving unconstrained optimization problems. Topics include line search, trust regions and derivative-free methods for unconstrained optimization. For constrained optimization the Karush-Kuhn-Tucker theory and basic solution techniques are presented. The close connection to Machine Learning and stochastic gradient descent is discussed.

Learning Outcomes

On completion of the course INF272 the candidate will have the following learning outcomes.

The candidate

  • can explain what a continuous optimization problem is and how it can be solved.
  • can explain the mathematical theory behind the solution algorithms for continuous optimization problems..
  • can analyze the effectivity of solution methods for continuous optimization problems.
  • can discuss the connection to Machine Learning.

Required Previous Knowledge

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

Recommended Previous Knowledge

INF270, MAT112

Credit Reduction due to Course Overlap

I274: 10 ECTS

Access to the Course

Access to the course requires admission to a programme of study at The Faculty of Mathematics and Natural Sciences

Teaching and learning methods

Lectures / 4 hours per week

Exercises / 2 hours per week

Compulsory Assignments and Attendance


Compulsory assignments are valid two semesters, the semester of the approval and the following semester.

Forms of Assessment

Oral exam.

Examination Support Material


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.

Reading List

The reading list will be available within June 1st for the autumn semester and December 1st for the spring semester

Course Evaluation

The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department

Programme Committee

The Programme Committee is responsible for the content, structure and quality of the study programme and courses.

Course Coordinator

Course coordinator and administrative contact person can be found on Mitt UiB, or contact mailto:studieveileder@ii.uib.no">Student adviser

Course Administrator

The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.


Student adviser:


T: 55 58 42 00