Syllabus for |
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| TDA251 - Algorithms, advanced course |
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| Syllabus adopted 2012-02-18 by Head of Programme (or corresponding) |
| Owner: MPALG |
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7,5 Credits |
| Grading: TH - Five, Four, Three, Not passed |
| Education cycle: Second-cycle |
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Major subject: Computer Science and Engineering, Information Technology
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Department: 37 - COMPUTER SCIENCE AND ENGINEERING
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Teaching language: English
Open for exchange students
Block schedule:
C
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0107 |
Project |
7,5 c |
Grading: TH |
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7,5 c
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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.