- Examples of applications of optimization and modelling training.
- Basic concepts and theory for optimization, in particular theory for convex problems.
- Linear algebra in Rn, in particular bases for the four fundamental subspaces corresponding to a given matrix, and LDLT-factorization of a symmetric positive semidefinite matrix.
- Linear optimization, including duality theory.
- Optimization of flows in networks.
- Quadratic optimization with linear equality constraints.
- Linear least squares problems, in particular minimum norm solutions.
- Unconstrained nonlinear optimization, in particular nonlinear least squares problems.
- Optimality conditions for constrained nonlinear optimization, in particular for convex problems.
- Lagrangian relaxation.
SF1811 Optimization 6.0 credits

SF1811 is a basic course on optimization.
Information per course offering
Information for Autumn 2025 Start 27 Oct 2025 programme students
- Course location
KTH Campus
- Duration
- 27 Oct 2025 - 12 Jan 2026
- Periods
Autumn 2025: P2 (6 hp)
- Pace of study
33%
- Application code
50285
- 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
- No information inserted
- Planned modular schedule
- P2: A1, D1, E1, H1, B2.
- Schedule
- Part of programme
Degree Programme in Energy and Environment, year 3, MHI
Degree Programme in Energy and Environment, year 3, HSS
Degree Programme in Energy and Environment, year 3, RENE
Master of Science in Engineering and in Education, year 5, TEDA
Degree Programme in Energy and Environment, year 3, ITH, Mandatory
Degree Programme in Energy and Environment, year 3, SMCS
Degree Programme in Energy and Environment, year 3, KEM
Master of Science in Engineering and in Education, year 4, MAFY
Degree Programme in Energy and Environment, year 3, MES
Degree Programme in Energy and Environment, year 3, SUE
Master of Science in Engineering and in Education, year 4, TEDA
Degree Programme in Energy and Environment, year 3, SUT
Master's Programme, Computer Science, year 2, CSDA
Master of Science in Engineering and in Education, year 5, MAFY
Master's Programme, Applied and Computational Mathematics, year 1
Master's Programme, Machine Learning, year 1
Degree Programme in Engineering Mathematics, year 3, Mandatory
Master's Programme, Machine Learning, year 2
Bachelor's Programme in Information and Communication Technology, year 3
Master's Programme, Systems, Control and Robotics, year 2
Master's Programme, Systems, Control and Robotics, year 1
Degree Programme in Industrial Technology and Sustainability, year 3, Mandatory
Contact
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus SF1811 (Autumn 2019–)Content and learning outcomes
Course contents
Intended learning outcomes
After completing the course students should for a passing grade be able to
- Apply basic theory, concepts and methods, within the parts of optimization theory described by the course content, to solve problems
- Formulate simplified application problems as optimization problems and solve using software.
- Read and understand mathematical texts about for example, linear algebra, calculus and optimization and their applications, communicate mathematical reasoning and calculations in this area, orally and in writing in such a way that they are easy to follow.
For higher grades the student should also be able to
- Explain, combine and analyze basic theory, concepts and methods within the parts of optimization theory described by the course content.
Literature and preparations
Specific prerequisites
Completed course in SF1624 Linear algebra and geometry or SF1672 Linear Algebra.
Completed course in SF1626 Calculus in several variables or SF1674 Multivariable Calculus.
Completed course in Numerical analysis, SF1511, SF1519, SF1545 or SF1546.
Literature
The literature is published on the course webpage no later than four weeks before the course starts.
Examination and completion
Grading scale
Examination
- INL1 - Home assignment, 2.0 credits, grading scale: P, F
- TEN2 - Exam, 4.0 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.
If the course is discontinued, students may request to be examined during the following two academic years.
The examiner decides, in consultation with KTHs Coordinator of students with disabilities (Funka), about any customized examination for students with documented,lastingdisability. The examiner may allow another form of examination for reexamination of 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.