TAOP62 · 6 credits · Basic level (G2F) · Matematik, Tillämpad matematik
Operations Research, Extended Course
Active · next start VT 2027
75%
passed the latest ordinary exam
TEN1 · 27 Mar 2026 · 191 students
71%
passed the ordinary exams, all sittings over the last 5 years
TEN1 · 922 students
51%
Harder than 51% of courses at basic level
Compared with 874 courses at the same level, ordinary exams over the last three academic years. 396 students.
Part of 1 programme
Exam
TEN1 Written exam
- 5
- 4
- 3
- U
Ordinary exams
71% passed · 922 students
- 75%191
- 81%205
- 71%177
- 73%166
- 53%183
Re-exams
59% passed · 475 students
- 56%18
- 76%46
- 91%22
- 61%46
- 52%29
- 54%69
- 70%50
- 34%61
- 57%37
- 59%97
- Pass the re-exam: 59% (475 students), compared with 71% (922 students) on the ordinary exam
- Get 4 or 5: 18% (1,397 students)
Show small sittings (2)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%1
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
- U
Main rounds
100% passed · 760 students
- 100%195
- 100%153
- 100%174
- 100%104
- 100%134
Later rounds
99% passed · 165 students
- 100%6
- 100%10
- 100%11
- 100%9
- 97%31
- 100%36
- 100%62
- Pass a later round: 99% (165 students), compared with 100% (760 students) on the ordinary exam
Show small sittings (3)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%2
- 100%1
About the course
Aim
Optimization deals with mathematical theory and methods aiming at analyzing and solving decision problems that arise in technology, economy, medicine, etc. The course gives a broad orientation of the field of optimization, with emphasis on basic theory and methods for discrete optimization problems in finite dimension, and it also gives some insight into its use for analyzing practical optimization problems. After the course, the student shall:
- be able to explain important classes of optimization problems and to be able to classify them according to their properties, into, for example, linear and discrete problems
- be able to model mathematical models of simple optimization problems
- be able to explain basic concepts, such as, for example, optimal conditions, valid inequalities, weak and strong duality
- have knowledge about and be able to apply basic theory for some common types of optimization problems, such as, for example, duality theory for linear optimization problems, and have knowledge about and be able to use optimality conditions, such as, for example, Bellman-conditions, to determine the optimality of a given solution
- be able to explain and to apply basic principles for solving some common types of optimization problems, such as, for example, the branch-and-bound method for discrete problems
- be able to use relaxations, and especially Lagrangian duality, to approximate optimization problems, and be able to estimate the optimal objective value through lower and upper bounds
- be able to use commonly available software for solving optimization problems of standard type
- have some knowledge of practical applications of optimization
Content
- Network optimization: Shortest path problems, maximum flow problems, minimum cost network flow problems, the network simplex method, integer problems with graph structure.
- Integer programming: Model formulation, branch-and-bound methods, cutting plane methods, applications to special structured integer problems.
- Dynamic programming: Formulation of deterministic problems, the principle of optimality, applications to network, inventory and resource allocation problems
- Examiner
- Jan Rolfes
- Scheduled hours
- 54 h
- Main field of study
- Matematik, Tillämpad matematik