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SF2812 Applied Linear Optimization 7.5 credits

About course offering


For course offering

Spring 2025 Start 14 Jan 2025 programme students

Application code



For course offering

Spring 2025 Start 14 Jan 2025 programme students


Jan Kronqvist (


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Course coordinator

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Headings with content from the Course syllabus SF2812 (Spring 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • The simplex method and interior methods for linear programming.
  • Utilization of problem structure in linear programming, e.g., decomposition and column generation.
  • Stochastic programming: methods and utilization of problem structure.
  • Branch-and-bound methods for integer programming.
  • Lagrangian relaxation and subgradient methods for large-scale integer programming problems with special structure.

Intended learning outcomes

To pass the course, the student shall 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 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 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

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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

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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


Education cycle

Second cycle

Add-on studies

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Jan Kronqvist (