- The simplex method and interior methods for linear programming.
- Utilization of problem structure in linear programming, e.g., decomposition and column generation.
- Stochastic programming: methods and utilization of problem structure.
- Branch-and-bound methods for integer programming.
- Lagrangian relaxation and subgradient methods for large-scale integer programming problems with special structure.
SF2812 Applied Linear Optimization 7.5 credits
Information per course offering
Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.
Information for Spring 2026 Start 13 Jan 2026 programme students
- Course location
KTH Campus
- Duration
- 13 Jan 2026 - 13 Mar 2026
- Periods
- P3 (7.5 hp)
- Pace of study
50%
- Application code
60860
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
Elective for all programmes as long as it can be included in your programme.
- Planned modular schedule
- [object Object]
- Schedule
- Schedule is not published
- Part of programme
Degree Programme in Engineering Mathematics, åk 3, Optional
Master's Programme, Aerospace Engineering, åk 1, Optional
Master's Programme, Aerospace Engineering, åk 1, SYS, Conditionally Elective
Master's Programme, Applied and Computational Mathematics, åk 1, Conditionally Elective
Master's Programme, Applied and Computational Mathematics, åk 1, OPST, Conditionally Elective
Master's Programme, Applied and Computational Mathematics, åk 2, Conditionally Elective
Master's Programme, Applied and Computational Mathematics, åk 2, OPST, Conditionally Elective
Master's Programme, Electric Power Engineering, åk 1, Recommended
Master's Programme, Electric Power Engineering, åk 2, Recommended
Master's Programme, Industrial Engineering and Management, åk 1, OSYT, Mandatory
Master's Programme, Mathematics, åk 1, Optional
Master's Programme, Systems, Control and Robotics, åk 1, Recommended
Master's Programme, Systems, Control and Robotics, åk 2, Recommended
Contact
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus SF2812 (Spring 2022–)Headings with content from the Course syllabus SF2812 (Spring 2022–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
To pass the course, the student shall be able to:
- Apply theory, concepts and methods from the parts of optimization that are given by the course contents to solve problems.
- Model, formulate and analyze simplified practical problems as optimization problems and solve by making use of given software.
- Collaborate with other students and demonstrate ability to present orally and in writing.
To receive the highest grade, the student should in addition be able to do the following:
- Combine and explain the methods in the course, and
- Apply and explain the theory and the concepts of the course in the practical problems that are included.
Literature and preparations
Specific prerequisites
- English B / English 6
- Completed basic course in optimization (SF1811, SF1861 or equivalent)
- Completed basic course in mathematical statistics (SF1914, SF1918, SF1922 or equivalent)
- Completed basic course in numerical analysis (SF1544, SF1545 or equivalent)
- Completed basic course in differential equations (SF1633, SF1683 or equivalent).
Literature
You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
A, B, C, D, E, FX, F
Examination
- PRO1 - Project, 1.5 credits, grading scale: A, B, C, D, E, FX, F
- PRO2 - Project, 1.5 credits, grading scale: A, B, C, D, E, FX, F
- TEN1 - Examination, 4.5 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Main field of study
Mathematics
Education cycle
Second cycle