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SF2822 Applied Nonlinear Optimization 7.5 credits

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Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.


For course offering

Spring 2025 Start 17 Mar 2025 programme students

Application code


Headings with content from the Course syllabus SF2822 (Spring 2022–) are denoted with an asterisk ( )

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 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.


No information inserted


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

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


  • 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

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted


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


Education cycle

Second cycle

Add-on studies

No information inserted


Anders Forsgren (