TAOP24 · 6 credits · Basic level (G2F) · Matematik, Tillämpad matematik
Optimization, Advanced Course
Active · next start VT 2027
77%
passed the latest ordinary exam
TEN1 · 4 Jun 2026 · 43 students
83%
passed the ordinary exams, all sittings over the last 5 years
TEN1 · 127 students
45%
Harder than 45% of courses at basic level
Compared with 874 courses at the same level, ordinary exams over the last three academic years. 74 students.
Part of 5 programmes
Exam
TEN1 Written examination
- 5
- 4
- 3
- U
Ordinary exams
83% passed · 127 students
- 77%43
- 87%31
- 93%29
- 79%24
Re-exams
87% passed · 30 students
- 92%12
- 92%13
- 60%5
- Pass the re-exam: 87% (30 students), compared with 83% (127 students) on the ordinary exam
- Get 4 or 5: 45% (157 students)
Show small sittings (7)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%4
- 0%2
- 75%4
- 50%4
- 100%1
- 100%1
- 100%4
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB1 Laboratory course
- G
Main rounds
100% passed · 106 students
- 100%40
- 100%39
- 100%27
Later rounds
100% passed · 69 students
- 100%53
- 100%16
- Pass a later round: 100% (69 students), compared with 100% (106 students) on the ordinary exam
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
About the course
Aim
The student must after completing the course
- M1 (modelling):have the ability to construct mathematical models for non-standard optimization problems in network optimization and integer optimization,
- M2 (theory and method):be able to identify optimization problems from the sub-areas included in the course and be able to use concepts, theory and methods within these areas,
- M3 (accounting):be able to report how modeling is done and methods are used within the sub-areas of the course, using correct concepts,
- M4 (software):be able to use standard optimization software for optimization problems.
Content
Network Optimization:network modeling, LP formulation of flows in networks, shortest path, minimum cost flows, maximum flows.
Dynamic programming:the optimality principle, formulation, deterministic dynamic programming.
Integer optimization:integer modeling, relaxations, heuristics.
Multi-objective optimization:Pareto optimality and Pareto front, scalarization, exposed Pareto points.
Nonlinear optimization:optimality conditions, gradient-based methods, quadratic programming.
Optimization under Uncertainty: robust optimization and stochastic optimization.
- Examiner
- Jan Rolfes
- Scheduled hours
- 40 h
- Main field of study
- Matematik, Tillämpad matematik