TAOP04 · 6 credits · Advanced level (A1N) · Matematik, Tillämpad matematik
Mathematical Optimization
Active · next start HT 2026
100%
passed the latest regular sitting
UPG1 · 19 Jan 2026 · 19 students
100%
passed the regular sittings, all sittings over the last 5 years
UPG1 · 39 students
Hand-in or lab
UPG1 Project
- 5
- 4
- 3
- U
Main rounds
100% passed · 39 students
- 100%19
- 100%20
Later rounds
71% passed · 7 students
- 71%7
- Pass a later round: 71% (7 students), compared with 100% (39 students) on the ordinary exam
- Get 4 or 5: 63% (46 students)
Who takes the course?
- Master of Science in Industrial Engineering and Management
- Master of Science in Information Technology
- Master of Science in Applied Physics and Electrical Engineering
- Master of Science in Applied Physics and Electrical Engineering
- Master of Science in Applied Physics and Electrical Engineering
- Master of Science in Engineering Mathematics
- Master's Programme in Mathematics
- Master's Programme in Mathematics
About the course
Aim
The course intends to give a deeper knowledge of mathematical optimization, with emphasis on solution methods for nonlinear optimization. After the course, the student shall be able to:
- reproduce and apply basic convergence theory for nonlinear optimization
- describe and apply some basic solution principles for nonlinear optimization, such as, for example, conjugate gradient methods
- understand the structure and function of commonly used computer software for nonlinear optimization.
Content
Algorithmic mappings, line-search methods, the simplex method for unconstrained optimization, conjugate gradient methods, quasi-Newton methods, sequential linear programming, sequential quadratic programming, model predictive control (MPC), Gauss-Newton methods.
- Examiner
- Chuan He
- Scheduled hours
- 36 h
- Main field of study
- Matematik, Tillämpad matematik