93MA53 · 4 credits · Basic level (GXX) · Matematik
Mathematcs: Intriduction to Operations Research
Active · next start VT 2027
81%
passed the latest regular sitting
STN2 · 29 May 2026 · 16 students
72%
passed the regular sittings, all sittings over the last 5 years
STN2 · 68 students
Part of 1 programme
Other module
STN2 Written examination
- VG
- G
- U
Main rounds
72% passed · 68 students
- 81%16
- 83%12
- 92%12
- 53%15
- 54%13
Later rounds
0% passed · 6 students
- 0%6
- Pass a later round: 0% (6 students), compared with 72% (68 students) on the ordinary exam
- Get VG: 14% (74 students)
Show small sittings (10)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 0%2
- 33%3
- 0%1
- 33%3
- 100%1
- 75%4
- 100%1
- 50%4
- 25%4
- 75%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 Work
- G
Main rounds
100% passed · 58 students
- 100%14
- 100%12
- 100%12
- 100%12
- 100%8
Later rounds
No sittings with at least 5 students.
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%2
About the course
Aim
After completing the course, the student should be able to
- M1 (Modelling):identify questions of an optimization nature in order to construct mathematical models for simpler optimization problems
- M2 (Theory and Method):use the basic concepts and theorems described by the course content, as well as choose and apply appropriate methods in order to solve commonly occurring optimization problems
- M3 (Accounting):argue for their approaches using relevant concepts within the course, and present clear calculations and reasoning
- M4 (Software):use available optimization software to solve standard optimization problems.
Content
Within optimization theory, mathematical theories and methods are treated that aim to analyze and solve decision problems that arise in technology, economics, medicine, etcetera. The course provides a broad orientation to optimization theory, but a focus on basic theory and methods for continuous optimization problems in finite dimension, as well as an insight into its application to analyze practical optimization questions. The course content includes:
- General optimality concepts: Basic convexity theory for functions and sets, optimality conditions, local and global optima, relaxation and restriction.
- Linear programming: Linear optimization models with continuous decision variables, graphical solution, mathematical theory of linear programming, the simplex method, duality, sensitivity analysis.
- Nonlinear programming: Nonlinear optimization models continuous decision variables, with/without subconditions, steepest slope method, Newton's (modified) method, exact line search, interval bisection, the Karush-Kuhn-Tucker conditions.
- Examiner
- Nils-Hassan Quttineh
- Main field of study
- Matematik