Non-Linear Optimization

Semester of Instruction

Irregular

Objectives 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.

Required Previous Knowledge

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

Recommended Previous Knowledge

INF270, MAT112

Compulsory Assignments and Attendance

Exercises.

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

Forms of Assessment

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.

Subject Overlap

I 274: 10 ECTS