- Unconstrained optimization: optimality conditions: Newton methods, quasi-Newton methods, conjugate gradient methods.
- Constrained optimization: optimality conditions, quadratic programming, sequential quadratic programming, barrier methods, primal-dual interior methods.
- Semidefinite programming including interior methods.
- Convexity and convex relaxations.
SF2822 Applied Nonlinear 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 2025 Start 17 Mar 2025 programme students
- Course location
KTH Campus
- Duration
- 17 Mar 2025 - 2 Jun 2025
- Periods
- P4 (7.5 hp)
- Pace of study
50%
- Application code
60422
- 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
Master's Programme, Aerospace Engineering, åk 1, Optional
Master's Programme, Aerospace Engineering, åk 1, SYS, Mandatory
Master's Programme, Applied and Computational Mathematics, åk 1, Optional
Master's Programme, Applied and Computational Mathematics, åk 1, OPST, Conditionally Elective
Master's Programme, Applied and Computational Mathematics, åk 2, OPST, Conditionally Elective
Master's Programme, Computer Simulations for Science and Engineering, åk 1, Conditionally Elective
Master's Programme, Electric Power Engineering, åk 1, Recommended
Master's Programme, Industrial Engineering and Management, åk 1, OSYT, Conditionally Elective
Master's Programme, Information and Network Engineering, åk 1, Recommended
Master's Programme, Mathematics, åk 1, Optional
Master's Programme, Systems, Control and Robotics, åk 1, LDCS, Conditionally Elective
Master's Programme, Systems, Control and Robotics, åk 2, LDCS, Conditionally Elective
Contact
Anders Forsgren (andersf@kth.se)
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus SF2822 (Spring 2022–)Content and learning outcomes
Course contents
Intended learning outcomes
To pass the course, the student should be able to do the following:
- 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 useof 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 coursein 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).
Recommended prerequisites
A completed continuationcourse in numerical analysis.
Equipment
Literature
To be announced at the beginning of the course. Preliminary literature:
Linear and Nonlinear Programming by S.G.Nash och A.Sofer, McGraw-Hill, and some material from the department.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
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.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
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.