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Dette er studietilbudet for studieår 2017-2018. 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
Course teacher
Antorweep Chakravorty, Leander Nikolaus Jehl
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

PhD studies at the Faculty of Science and Technology

Course assessment

Through forms for student evaluation and/or discussions with the students, following the standard guidelines.

Literature

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

(about 500 pages)

Additional notes (about 100 side)



Dette er studietilbudet for studieår 2017-2018. Endringer kan komme.

Sist oppdatert: 23.01.2018