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Dette er studietilbudet for studieår 2018-2019. Endringer kan komme.


This course is about algorithm theory and complexity theory, which includes the following topics: Graphs and graph algorithms, greedy algorithms, dynamic programming, linear programming, and NP-completeness.

Learning outcome

After completing this course the student should be able to:
  • Understand what algorithms and datastructures mean for developing lage and complex information systems
  • Create efficient algorithms, in terms of time, and resource like memory
  • Choose and apply different types of algorithms depending on what the information systems demand
  • Choose the optimal algorithms among many competing ones

Contents

Introduction to algorithm theory and complexity theory; Sorting and order statistics, datastructures , advanced design and analysis techniques, graphs and graph algorithms, multithreaded algorithms, NP-completeness.

Required prerequisite knowledge

None.

Recommended previous knowledge

DAT200 Algorithms and Datastructures

Exam

Weight Duration Marks Aid
Written exam1/14 hoursA - FNo printed or written materials are allowed. Approved basic calculator allowed.

Coursework requirements

Compulsory assignments
4 compulsory assignments.

Subject teacher(s)

Course coordinator
Reggie Davidrajuh
Head of Department
Tom Ryen

Method of work

4 hours lectures and 2 hours exercises.

Overlapping courses

Course Reduction (SP)
Algorithm Theory (MID290_1) 10

Open to

Master studies at the Faculty of Science and Technology

Course assessment

Form and/or discussion.

Literature

Cormen et al, "Introduction to Algorithms", MIT Press, 2009

(about 500 pages)

Additional notes (about 100 side)



Dette er studietilbudet for studieår 2018-2019. Endringer kan komme.

Sist oppdatert: 21.04.2018