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FSF3840 Numerical Nonlinear Programming 7.5 credits

About course offering

For course offering

Autumn 2023 Start 28 Aug 2023 programme students

Target group

Only PhD students.

Part of programme

No information inserted


P1 (3.0 hp), P2 (4.5 hp)


28 Aug 2023
15 Jan 2024

Pace of study


Form of study

Normal Daytime

Language of instruction


Course location

KTH Campus

Number of places

Places are not limited

Planned modular schedule


For course offering

Autumn 2023 Start 28 Aug 2023 programme students

Application code



For course offering

Autumn 2023 Start 28 Aug 2023 programme students


Anders Forsgren (


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Course coordinator

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Headings with content from the Course syllabus FSF3840 (Spring 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course deals with algorithms and fundamental theory for nonlinear finite-dimensional optimization problems. Fundamental optimization concepts, such as convexity and duality are also introduced.

The main focus is nonlinear programming, unconstrained and constrained. Areas considered are unconstrained minimization, linearly constrained minimization and nonlinearly constrained minization. The focus is on methods which are considered modern and efficient today.

Linear programming is treated as a special case of nonlinear programming.

Semidefinite programming and linear matrix inequalities are also covered.

Intended learning outcomes

That the student should obtain a deep understanding of the mathematical theory and the numerical methods for nonlinear programming.

After completed course, the student should be able to

  • Derive optimality conditions for different classes of nonlinear optimization problems.

  • Explain how the method of steepest descent, the method of conjugate gradients, quasi-Newton methods and Newton methods work for unconstrained optimization, both linesearch methods and trust-region methods

  • Explain methods related to the above for equality-constrained problems

  • Explain methods related to the above for inequality-constrained problems

  • Explain how interior methods for semidefinite programming work

Literature and preparations

Specific prerequisites

A Master degree including at least 30 university credits (hp) in in Mathematics (Calculus, Linear algebra, Differential equations and transform method), and further at least  6 hp in Mathematical Statistics, 6 hp in Numerical analysis and 6 hp in Optimization.

Recommended prerequisites

Suitable prerequisites are the courses SF2822 Applied Nonlinear Optimization, SF2520 Applied Numerical Methods and SF2713 Foundations of Analysis, or similar knowledge.


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Announced when the course is offered.

Examination and completion

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

Grading scale

P, F


  • INL1 - Assignment, 7.5 credits, grading scale: P, 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.

The examination is by homework assignments and a final oral exam.

Other requirements for final grade

Homework assignments and a final oral exam.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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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

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted


Anders Forsgren (

Postgraduate course

Postgraduate courses at SCI/Mathematics