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Syllabus for

Academic year
TDA251 - Algorithms, advanced course
 
Syllabus adopted 2012-02-18 by Head of Programme (or corresponding)
Owner: MPALG
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Computer Science and Engineering, Information Technology
Department: 37 - COMPUTER SCIENCE AND ENGINEERING


Teaching language: English
Open for exchange students
Block schedule: C

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0107 Project 7,5 c Grading: TH   7,5 c    

In programs

MPALG COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 2 (elective)
MPALG COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 1 (compulsory elective)
MPCSN COMPUTER SYSTEMS AND NETWORKS, MSC PROGR, Year 2 (elective)
MPCSN COMPUTER SYSTEMS AND NETWORKS, MSC PROGR, Year 1 (elective)
MPSOF SOFTWARE ENGINEERING, MSC PROGR, Year 2 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 2 (elective)

Examiner:

Professor  Devdatt Dubhashi
Bitr professor  Peter Damaschke


Replaces

TDA250   Algorithms, advanced course

Course evaluation:

http://document.chalmers.se/doc/ca129022-04ca-4238-bbab-744c07ca7e7c


  Go to Course Homepage

Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

The course TIN092 Algorithms or equivalent is required.

Aim

The course provides advanced techniques in the design and analysis of algorithms. It continues in the spirit of the first Algorithms course and maintains a rigorous analytical style. The course goes deeper into specialized topics in algorithms. At some points it may even touch on frontiers of current research.

Learning outcomes (after completion of the course the student should be able to)

- know in more depth some important design and analysis techniques for algorithms, in particular, ways to approach NP-complete problems
- to some extent be able to apply such techniques to solve new problems that may arise in various applications
- have some practice in recognizing connections between algorithmic problems and reducing them to each other
- be able to explain more complex algorithms and proofs in written form
- know selected topics of current research on algorithms

Content

- approximation algorithms and their analysis, approximation schemes,
- use of linear programming, in particular for approximation,
- network flow with some complex applications,
- randomized algorithms and their analysis by appropriate random variables,
- helpful input structures like tree structures and input parameters

Organisation

Lectures and hand-in exercises.

Literature

See separate literature list.

Examination

Exam, consisting of hand-in exercises and a final take-home exam.


Page manager Published: Mon 28 Nov 2016.