Syllabus for |
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TDA206 - Discrete optimization
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| Diskret optimering |
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| Syllabus adopted 2019-02-08 by Head of Programme (or corresponding) |
| Owner: MPALG |
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7,5 Credits
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| Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail |
| Education cycle: Second-cycle |
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Major subject: Computer Science and Engineering, Software Engineering
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Department: 37 - COMPUTER SCIENCE AND ENGINEERING
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Teaching language: English
Application code: 02131
Open for exchange students: Yes
Block schedule:
A
Maximum participants: 60
| Module |
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0101 |
Examination |
7,5 c |
Grading: TH |
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7,5 c
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17 Mar 2021 am J, |
25 Aug 2021 pm J
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In programs
MPALG COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 1 (compulsory elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory elective)
MPCSN COMPUTER SYSTEMS AND NETWORKS, MSC PROGR, Year 1 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
Examiner:
Devdatt Dubhashi
Go to Course Homepage
Eligibility
General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
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
Written exam.