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Syllabus for

Academic year
TDA206 - Discrete optimization
 
Syllabus adopted 2013-02-09 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: A

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0101 Examination 7,5 c Grading: TH   7,5 c   Contact examiner

In programs

MPCSN COMPUTER SYSTEMS AND NETWORKS, MSC PROGR, Year 1 (elective)
TKITE SOFTWARE ENGINEERING, Year 3 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory elective)
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)

Examiner:

Professor  Devdatt Dubhashi
Bitr professor  Peter Damaschke


Course evaluation:

http://document.chalmers.se/doc/b3fc18d1-2fb4-4df9-a2b8-6d3680908bbd


  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

Mathematics (including Discrete mathematics and Linear algebra), Programming, Algorithms and/or data structures.

Aim

The topic of the course is the theory and practice of optimization problems over discrete structures, and has strong connections to Optimization Theory (linear programming), Computer Science (algorithms and complexity), and Operational Research. Problems of this kind arise in many different contexts including transportation, telecommunications, industrial planning, finance, bioinformatics, hardware design and cryptology.
A characteristic property of these problems are that they are difficult to solve. The course intends to develop the skill of modelling real problems and to use mathematical and algorithmic tools to solve them, optimally or heuristically.

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

- identify optimization problems in various application domains,
- formulate them in exact mathematical models that capture the essentials
of the real problems but are still manageable by computational methods,
- assess which problem class a given problem belongs to,
- apply linear programming, related generic methods, and additional
heuristics, to computational problems,
- explain the geometry of linear programming,
- dualize optimization problems and use the dual forms to obtain bounds,
- work with the scientific literature in the field.

Content

modelling, linear programs and integer linear programs, polytopes, duality in
optimization, branch-and-bound and other heuristics, some special graph
algorithms

Organisation

Lectures and homework assignments.

Literature

See separate literature list.

Examination

Assignments and home exam. Grading scale U, 3, 4 or 5.


Page manager Published: Mon 28 Nov 2016.