TAOP34 · 6 credits · Advanced level (A1N) · Matematik, Tillämpad matematik
Large Scale Optimization
Active · next start HT 2026
100%
passed the latest regular sitting
UPG1 · 11 Nov 2025 · 17 students
100%
passed the regular sittings, all sittings over the last 5 years
UPG1 · 71 students
This module only reports passing results, so the pass rate does not show how many did not pass.
Part of 5 programmes
Hand-in or lab
UPG1 Exercise
- G
Main rounds
100% passed · 71 students
- 100%17
- 100%21
- 100%13
- 100%10
- 100%10
Later rounds
No sittings with at least 5 students.
Show small sittings (9)
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
- 100%2
- 100%2
- 100%1
- 100%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.
Other module
MUN1 Examination
- 5
- 4
- 3
Main rounds
100% passed · 71 students
- 100%11
- 100%21
- 100%14
- 100%11
- 100%14
Later rounds
No sittings with at least 5 students.
- Get 4 or 5: 27% (71 students)
Show small sittings (8)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%2
- 100%1
- 100%1
- 100%1
- 100%1
- 100%1
- 100%1
- 100%1
About the course
Aim
The course aims at giving insight into the practical application of optimization methodology to technical and economic decision problems, and to give knowledge about solution principles for certain classes of structured large-scale optimization problems that frequently arise in practical applications. After the course, the student shall:
- be able to state and describe the mathematical principles that are used to decompose optimization problems
- be able to apply decomposition methods to solve structured optimization problems
- be acquainted with applications of decomposition methods, be able to identify applications that are well suited for such methods, and be able to choose a suitable methodology and use thereof
- have an enhanced knowledge of the practical use of optimization methodology.
Content
Advanced linear programming and column generation. Price-directive decentralized planning and the Dantzig-Wolfe decomposition principle. Lagrangean relaxation and subgradient optimization. Application to problems arising in for example facility location and in the planning of production and distribution.
- Examiner
- Torbjörn Larsson
- Scheduled hours
- 60 h
- Main field of study
- Matematik, Tillämpad matematik