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FSF3846 Combinatorial Optimization 7.5 credits

Information per course offering

Course offerings are missing for current or upcoming semesters.

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus FSF3846 (Spring 2019–)
Headings with content from the Course syllabus FSF3846 (Spring 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Study of some fundamental combinatorial optimization problems: algorithms, complexity and applications.

Algorithms: Maxflow-mincut-theorem. Primal-dual method for linear programming, with applications to network flows. Efficient algorithms for maxflow problems. Matching. Minimal spanning trees. Matroids.

Complexity: NP-completeness, foundations and relevant examples.

Applications: Heuristic methods for some interesting problem classes.

Intended learning outcomes

That the student should obtain a deep understanding of the mathematical theory and some practical algorithms for combinatorial optimization.

After completed course, the student should be able to

  • Explain basic concepts of computational complexity.
  • Explain methods for fundamental network flow problems.
  • Explain methods for matching.
  • Explain fundamental concepts of integer programming.
  • Explain some important heuristic methods.

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.

Suitable prerequisites is the courses SF2812 Applied Linear Optimization or similar knowledge.

Equipment

No information inserted

Literature

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

Examination

  • 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

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

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.

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

Contact

Anders Forsgren (andersf@kth.se), Optimeringslära och systemteori, KTH.

Postgraduate course

Postgraduate courses at SCI/Mathematics