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Syllabus for

Academic year
TDA206 - Discrete optimization  
Diskret optimering
 
Syllabus adopted 2017-02-20 by Head of Programme (or corresponding)
Owner: MPALG
7,5 Credits
Grading: TH - Five, Four, Three, Fail
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: Yes
Block schedule: A
Maximum participants: 55

Course elements   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0101 Examination 7,5c Grading: TH   7,5c   20 Mar 2019 am M   Contact examiner

In programs

MPCSN COMPUTER SYSTEMS AND NETWORKS, MSC PROGR, Year 1 (elective)
TKITE SOFTWARE ENGINEERING, Year 3 (elective)
MPALG COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 1 (compulsory elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory elective)

Examiner:

Devdatt Dubhashi

  Go to Course Homepage


 

Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

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 and their geometric properties, duality in optimization, branch-and-bound and other heuristics, some special graph algorithms

Organisation

Lectures and homework assignments.

Literature

See separate literature list.

Examination including compulsory elements

Assignments and home exam.


Published: Fri 18 Dec 2009. Modified: Mon 28 Nov 2016