Program Overview

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Course Information

The course SF2822 Applied Nonlinear Optimization is a 7.5 credit course.

Information per Course Offering

• Termin: Spring 2026
• Start: 16 Mar 2026
• Duration: 16 Mar 2026 - 1 Jun 2026
• Periods: Spring 2026: P4 (7.5 hp)
• Pace of study: 50%
• Application code: 60873
• Form of study: Normal Daytime
• Language of instruction: English
• Course memo: Not published
• Number of places: Not limited
• Target group: Elective for all programmes as long as it can be included in your programme.
• Planned modular schedule: P4: B1, E1, F1, I1, F2.

Part of Programme

The course is part of the following programmes:

• Master's Programme, Systems, Control and Robotics, year 2, LDCS
• Master's Programme, Applied and Computational Mathematics, year 2, OPST
• Master's Programme, Applied and Computational Mathematics, year 1, OPST
• Master's Programme, Industrial Engineering and Management, year 1, OSYT
• Master's Programme, Applied and Computational Mathematics, year 1, CSSE
• Master's Programme, Systems, Control and Robotics, year 1, LDCS
• Master's Programme, Aerospace Engineering, year 1, SYS, Mandatory
• Master's Programme, Applied and Computational Mathematics, year 1
• Master's Programme, Aerospace Engineering, year 1
• Master's Programme, Electric Power Engineering, year 2
• Master's Programme, Information and Network Engineering, year 1
• Master's Programme, Electric Power Engineering, year 1
• Master's Programme, Mathematics, year 1

Contact

• Examiner: Anders Forsgren
• Course coordinator: Anders Forsgren
• Teachers: Anders Forsgren

Course Syllabus

The course syllabus is available as a PDF.

Content and Learning Outcomes

Course Contents

• 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.

Intended Learning Outcomes

To pass the course, the student should 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:
• 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)

Recommended Prerequisites

A completed continuation course in numerical analysis.

Literature

Information about course literature can be found in the course memo for the course offering or in the course room in Canvas.

Examination and Completion

Grading Scale

A, B, C, D, E, FX, F

Examination

• PRO2 - Project, 1.5 credits, grading scale: A, B, C, D, E, FX, F
• PRO1 - 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.

If the course is discontinued, students may request to be examined during the following two academic years.

Examiner

Anders Forsgren

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.

Offered by

SCI/Mathematics

Main Field of Study

Mathematics

Education Cycle

Second cycle